From 1b68b19dedbd6bedf6d60c010b00d584e8f78752 Mon Sep 17 00:00:00 2001 From: Jason DeBacker Date: Mon, 3 Aug 2026 16:33:11 -0400 Subject: [PATCH 01/14] gov't goods produced by multiple sectors --- CHANGELOG.md | 91 + docs/book/content/intro/parameters.md | 23 +- docs/book/content/theory/equilibrium.md | 4 +- docs/book/content/theory/firms.md | 2 +- docs/book/content/theory/government.md | 4 +- docs/book/content/theory/market_clearing.md | 8 +- docs/book/content/theory/stationarization.md | 2 +- examples/multi_industry_example.py | 4 +- ogcore/SS.py | 92 +- ogcore/TPI.py | 60 +- ogcore/__init__.py | 2 +- ogcore/aggregates.py | 134 +- ogcore/default_parameters.json | 580440 +++++++++++++++- ogcore/demographics.py | 486 +- ogcore/fiscal.py | 66 +- ogcore/household.py | 37 +- ogcore/model_variables.json | 20 +- ogcore/output_plots.py | 33 +- ogcore/output_tables.py | 4 +- ogcore/parameter_plots.py | 15 +- ogcore/parameters.py | 69 +- ogcore/pensions.py | 251 +- ogcore/tax.py | 42 +- ogcore/utils.py | 20 +- pyproject.toml | 2 +- scripts/QUICK_INSTALL.md | 1 + scripts/install.ps1 | 3 +- scripts/install.sh | 1 + scripts/repos.json | 7 + tests/BENCHMARK_README.md | 24 +- tests/test_SS.py | 22 +- tests/test_TPI.py | 20 +- tests/test_aggregates.py | 245 +- tests/test_basic.py | 8 +- tests/test_demographics.py | 226 +- tests/test_firm.py | 9 +- tests/test_fiscal.py | 15 + tests/test_household.py | 30 +- tests/test_output_plots.py | 4 +- tests/test_output_tables.py | 4 +- tests/test_parameter_plots.py | 22 +- tests/test_parameter_tables.py | 4 +- tests/test_parameters.py | 33 +- tests/test_pensions.py | 106 +- tests/test_tax.py | 158 +- tests/testing_params.json | 56816 +- uv.lock | 8 +- 47 files changed, 636632 insertions(+), 3045 deletions(-) diff --git a/CHANGELOG.md b/CHANGELOG.md index 69d9fb503..c2b377563 100644 --- a/CHANGELOG.md +++ b/CHANGELOG.md @@ -5,6 +5,92 @@ All notable changes to this project will be documented in this file. The format is based on [Keep a Changelog](https://keepachangelog.com/en/1.0.0/), and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0.html). +## [0.19.0] - 2026-07-29 12:00:00 + +### Added + +- Fixes Issue [#1169](https://github.com/PSLmodels/OG-Core/issues/1169): + the Notional Defined Contribution pension system could not run against + a real `Specifications` object because its growth-rate settings were + not parameters. `ndc_growth_rate`, `dir_growth_rate`, and + `points_growth_rate` are now parameters in `default_parameters.json` + (choices `"r"`, `"Curr GDP"`, `"LR GDP"`; default `"LR GDP"`, matching + the previous fallback behavior). `g_ndc` and `g_dir` now handle the + scalar `r` and `g_y` of the steady state, and `delta_ret` falls back + to the steady-state mortality rates implied by `rho` (averaged over + lifetime income groups) when no `mort_rates_SS` attribute is set. The + NDC system is added to the real-`Specifications` pension test. + +## [0.18.1] - 2026-07-22 12:00:00 + +### Bug Fixes + +- Fixes Issue [#1180](https://github.com/PSLmodels/OG-Core/issues/1180): + `get_pop_objs` raised an `AssertionError` for the homogeneous case + (`income_percentiles=None` with no income-specific inputs), because + `expand_pop_obj_J` asserted before reaching its homogeneous branch. + `income_percentiles` now defaults to a single income group (J=1) when + no income-specific inputs are supplied, restoring the pre-0.18 call + signature; it remains required whenever gradients or immigrant income + shares are provided. Also removes a stray debug `print` from that + branch. + +## [0.18.0] - 2026-07-20 12:00:00 + +### Added + +- Demographic parameters (`rho`, `omega`, `imm_rates`) are now allowed to + vary across lifetime income groups, `J`. See PR [#1165](https://github.com/PSLmodels/OG-Core/pull/1165) + +## [0.17.0] - 2026-07-16 12:00:00 + +### Bug Fixes + +- Fixes the Defined Benefits and Points System pension paths so they run + against a real `Specifications` object (Issues #1014 and #1075): + `p.retire` (an array since PR #433) was passed as the scalar `S_ret` + into the numba loops, the scalar `p.g_y` was indexed as an array inside + the loops, the scalar steady-state wage was indexed as a path, and the + time-varying 3-D `e` matrix (PR #895) was sliced as 2-D. The systems + use the steady-state retirement age and earnings profile for now; full + time variation remains open in Issue #1014. The same coercions are + applied to the NDC path, but the NDC system additionally requires + growth-rate settings (`ndc_growth_rate`, `dir_growth_rate`) that are + not yet parameters in `default_parameters.json`, so it still cannot + run against a real `Specifications` object (see Issue #1169). +- Wires the pre-time-path inputs the DB/NDC/PS benefit formulas need + into the TPI: labor supplied before the time path begins comes from + the model's initial condition (the same baseline object that + initializes wealth), and pre-time-path wages are anchored to the + period-0 wage of the current path. Adds the full time-path (T x S x J) + evaluation of Defined Benefits amounts (`DB_3dim_loop`) used when the + TPI computes aggregate revenues, built from each cohort's own wage and + labor history so aggregates are consistent with household behavior. + With these changes a country model using the Defined Benefits system + solves both the steady state and the transition path. +- Adds regression tests that call `pension_amount` with a real + `Specifications` object per pension system (the existing tests + pre-scalarized the inputs and so never exercised the real interface) + and a local-marked steady-state solve test with the Defined Benefits + system. See PR [#1167](https://github.com/PSLmodels/OG-Core/pull/1167). +- Fixes the tax-liability revenue calculation using the first year's tax + noncompliance and filer rates for every period, even when those rates + are set to vary over time (Issue #1168): households responded to the + changing rates while government revenue, the budget, and debt stayed on + the first-year values. `income_tax_liab` now slices the rates over the + transition path. See PR [#1174](https://github.com/PSLmodels/OG-Core/pull/1174). +- Fixes the steady-state `etr_ss` and `mtry_ss` diagnostics using the + labor-income tax noncompliance rate in place of the capital-income rate + (`capital_noncompliance_rate_2D` in `SS.py`, Issue #1170). This affects + only the post-solve SS diagnostics -- the solution itself already used + the correct rates -- and is a no-op when the two rates are equal (the + default). See PR [#1171](https://github.com/PSLmodels/OG-Core/pull/1171). +- Fixes `alpha_FA` (direct foreign aid, added in 0.16.0) not being + extended over the model time path: it was never registered in + `tp_param_list`, so a multi-year path stayed short and broke the + transition solve. It now extends to `T + S` with the last value carried + forward, like `alpha_G`/`alpha_T`/`alpha_I`. See PR [#1166](https://github.com/PSLmodels/OG-Core/pull/1166). + ## [0.16.4] - 2026-07-02 12:00:00 ### Added @@ -615,6 +701,11 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0 - Any earlier versions of OG-USA can be found in the [`OG-Core`](https://github.com/PSLmodels/OG-Core) repository [release history](https://github.com/PSLmodels/OG-Core/releases) from [v.0.6.4](https://github.com/PSLmodels/OG-Core/releases/tag/v0.6.4) (Jul. 20, 2021) or earlier. +[0.18.1]: https://github.com/PSLmodels/OG-Core/compare/v0.18.0...v0.18.1 +[0.18.0]: https://github.com/PSLmodels/OG-Core/compare/v0.17.0...v0.18.0 +[0.17.0]: https://github.com/PSLmodels/OG-Core/compare/v0.16.4...v0.17.0 +[0.16.4]: https://github.com/PSLmodels/OG-Core/compare/v0.16.3...v0.16.4 +[0.16.3]: https://github.com/PSLmodels/OG-Core/compare/v0.16.2...v0.16.3 [0.16.3]: https://github.com/PSLmodels/OG-Core/compare/v0.16.2...v0.16.3 [0.16.2]: https://github.com/PSLmodels/OG-Core/compare/v0.16.1...v0.16.2 [0.16.1]: https://github.com/PSLmodels/OG-Core/compare/v0.16.0...v0.16.1 diff --git a/docs/book/content/intro/parameters.md b/docs/book/content/intro/parameters.md index ada2ded14..57e641600 100644 --- a/docs/book/content/intro/parameters.md +++ b/docs/book/content/intro/parameters.md @@ -221,7 +221,7 @@ _Out-of-Range Action:_ error #### `io_matrix` -_Description:_ Input-output matrix used to map production outputs into consumption goods using a fixed coefficient model. This matrix has dimensions I x M, where I is the number of distinct consumption goods and M is the number of distinct production goods. The sum each row of this matrix must be 1. +_Description:_ Input-output matrix used to map production outputs into consumption and government goods using a fixed coefficient model. It has dimensions (I+2) x M. The first I rows map consumption goods, row I+1 maps government consumption, and row I+2 maps infrastructure investment. Each row sums to 1. _Value Type:_ float _Valid Range:_ min = 0.0 and max = 1.0 _Out-of-Range Action:_ error @@ -326,6 +326,20 @@ _Valid Range:_ min = 0.0 and max = 1.0 _Out-of-Range Action:_ error +#### `ndc_growth_rate` +_Description:_ Growth rate applied to contributions under a notional defined contribution system. +_Notes:_ 'r' uses the model interest rate, 'Curr GDP' the current GDP growth rate, and 'LR GDP' the long-run GDP growth rate. +_Value Type:_ str +_Valid Choices:_['r', 'Curr GDP', 'LR GDP'] + + +#### `dir_growth_rate` +_Description:_ Growth rate used in the conversion coefficient of a notional defined contribution system. +_Notes:_ 'r' uses the model interest rate, 'Curr GDP' the current GDP growth rate, and 'LR GDP' the long-run GDP growth rate. +_Value Type:_ str +_Valid Choices:_['r', 'Curr GDP', 'LR GDP'] + + #### `alpha_db` _Description:_ Replacement rate under a defined contribution system. _Value Type:_ float @@ -340,6 +354,13 @@ _Valid Range:_ min = 0.0 and max = 1.0 _Out-of-Range Action:_ error +#### `points_growth_rate` +_Description:_ Growth rate applied to contributions under a points system pension. +_Notes:_ 'r' uses the model interest rate, 'Curr GDP' the current GDP growth rate, and 'LR GDP' the long-run GDP growth rate. +_Value Type:_ str +_Valid Choices:_['r', 'Curr GDP', 'LR GDP'] + + #### `yr_contrib` _Description:_ Number of years of contributions made to defined benefits pension system. _Notes:_ Since there is not exit from the labor force in the model, the number of years of contributions is set exogenously. diff --git a/docs/book/content/theory/equilibrium.md b/docs/book/content/theory/equilibrium.md index 0a5306c37..79f420f6b 100644 --- a/docs/book/content/theory/equilibrium.md +++ b/docs/book/content/theory/equilibrium.md @@ -179,7 +179,7 @@ The computational algorithm for solving for the steady-state follows the steps b ``` 1. Make sure that steady-state government spending is nonnegative $\bar{G}\geq 0$. If steady-state government spending is negative, that means the government is getting resources to supply the debt from outside the economy each period to stabilize the debt-to-GDP ratio. $\bar{G}<0$ is a good indicator of unsustainable policies. - 1. Make sure that the resource constraint (goods market clearing) {eq}`EqStnrzMarkClrGoods_M` is satisfied. It is redundant, but this is a good check as to whether everything worked correctly. + 1. Make sure that the industry resource constraints are satisfied, including the input-output demands for government consumption and infrastructure goods. One goods-market condition is redundant, but it provides a useful solution check. 2. Make sure that the government budget constraint {eq}`EqStnrzGovBC` binds. 3. Make sure that all the $2JS$ household Euler equations are solved to a satisfactory tolerance. @@ -411,7 +411,7 @@ The stationary non-steady state (transition path) solution algorithm has followi 24. If the maximum absolute error among the M-1+5 outer loop error terms is less-than-or-equal-to some small positive tolerance $toler_{tpi,out}$ in each period along the transition path, $\max\big|\left(error_{r_p}, error_r, error_w, error_p, error_{bq},error_{tr}\right)\bigr| \leq toler_{tpi,out}$ then the non-steady-state equilibrium has been found. - 1. Make sure that the resource constraint for industry $M$ (goods market clearing) {eq}`EqStnrzMarkClrGoods_M` is satisfied in each period along the time path. It is redundant, but this is a good check as to whether everything worked correctly. + 1. Make sure that every industry resource constraint, including the input-output demands for government consumption and infrastructure goods, is satisfied in each period. One condition is redundant, but it provides a useful solution check. 2. Make sure that the government budget constraint {eq}`EqStnrzGovBC` binds in every period. 3. Make sure that all the $(T+S)\times2JS$ household Euler equations are solved to a satisfactory tolerance in every period. diff --git a/docs/book/content/theory/firms.md b/docs/book/content/theory/firms.md index c89308fcb..32902b53e 100644 --- a/docs/book/content/theory/firms.md +++ b/docs/book/content/theory/firms.md @@ -26,7 +26,7 @@ The production side of the `OG-Core` model is populated by $M$ industries indexe Y_{m,t} = Z_{m,t} (K_{m,t})^{\gamma_m} (K_{g,m,t})^{\gamma_{g,m}}(e^{g_y t}L_{m,t})^{1-\gamma_m-\gamma_{g,m}} \quad\forall m,t \quad\text{for}\quad \varepsilon_m=1 ``` -Industry $M$ in the model is unique in two respects. First, we will define industry $M$ goods as the numeraire in OG_Core. Therefore, all quantities are in terms of industry $M$ goods and all prices are relative to the price of a unit of industry $M$ goods. Second, the model solution is greatly simplified if just one production industry produces capital goods. The assumption in OG-Core is that industry $M$ is the only industry producing capital goods (though industry $M$ goods can also be used for consumption). +Industry $M$ in the model is unique in two respects. First, we define industry $M$ goods as the numeraire in OG-Core. Therefore, all prices are relative to the price of a unit of industry $M$ goods. Second, industry $M$ is the only industry producing private capital goods. Household consumption goods, government consumption goods, and infrastructure investment goods may each combine output from all $M$ industries through the input-output matrix. (EqFirmsFOC)= ## Optimality Conditions diff --git a/docs/book/content/theory/government.md b/docs/book/content/theory/government.md index 59b4eb475..f372445a6 100644 --- a/docs/book/content/theory/government.md +++ b/docs/book/content/theory/government.md @@ -619,10 +619,10 @@ Note that the budget closure rule (described in Section ref{`SecUnbalGBCcloseRul ```{math} :label: EqUnbalGBCbudgConstr - D_{t+1} + Rev_t + FA_t = (1 + r_{gov,t})D_t + G_t + I_{g,t} + Pensions_t + TR_t + UBI_t \quad\forall t + D_{t+1} + Rev_t + FA_t = (1 + r_{gov,t})D_t + p_{g,t}G_t + p_{I_g,t}I_{g,t} + Pensions_t + TR_t + UBI_t \quad\forall t ``` - where $r_{gov,t}$ is the interest rate paid by the government defined in equation {eq}`EqUnbalGBC_rate_wedge` below, $G_{t}$ is government spending on public goods, $I_{g,t}$ is total government spending on infrastructure investment, $TR_{t}$ are non-pension government transfers, and $UBI_t$ is the total UBI transfer outlays across households in time $t$. All variables in {eq}`EqUnbalGBCbudgConstr` are real variables denominated in units of current-period output in industry $M$ the numeraire ($p_{M,t}=1$ for all $t$). + where $G_t$ and $I_{g,t}$ are quantities of composite government consumption and infrastructure goods. Their prices are determined by the final two rows of the input-output matrix, $p_{g,t}=\sum_m\pi_{I+1,m}p_{m,t}$ and $p_{I_g,t}=\sum_m\pi_{I+2,m}p_{m,t}$. Other fiscal flows are denominated in units of industry $M$, the numeraire ($p_{M,t}=1$). We assume that government spending on public goods in terms of the numeraire good is a fixed fraction of GDP each period in the initial periods. diff --git a/docs/book/content/theory/market_clearing.md b/docs/book/content/theory/market_clearing.md index 7848937ca..df0332f70 100644 --- a/docs/book/content/theory/market_clearing.md +++ b/docs/book/content/theory/market_clearing.md @@ -107,17 +107,17 @@ C_{i,t} \equiv \sum_{s=E+1}^{E+S}\sum_{j=1}^{J}\omega_{s,t}\lambda_j c_{i,j,s,t} \quad\forall i,t ``` - Because we make a simplifying assumption that only the $M$th industry output can be used as investment, government spending, or government debt, consumption demand equals total output of good $m$ in the first $M-1$ industries. + Government consumption and infrastructure investment are also fixed-coefficient composites. Let $\pi_{I+1,m}$ and $\pi_{I+2,m}$ be the shares of industry $m$ used in one unit of those goods. Because private capital goods and net international financial flows remain assigned to industry $M$, the first $M-1$ goods-market conditions are ```{math} :label: EqMarkClrGoods_Mm1 - Y_{m,t} = C_{m,t} \quad\forall t \quad\text{and}\quad m=1,2,...M-1 + Y_{m,t} = C_{m,t} + \pi_{I+1,m}G_t + \pi_{I+2,m}I_{g,t} \quad\forall t \quad\text{and}\quad m=1,2,...M-1 ``` - The output of the $M$th industry can be used for private investment, infrastructure investment, government spending, and government debt.[^M_ind] As such, the market clearing condition in the $M$th industry will look more like the traditional $Y=C+I+G+NX$ expression.[^RCrates_note] + Industry $M$ additionally produces all private capital goods and absorbs the modeled net international financial flows.[^M_ind] ```{math} :label: EqMarkClrGoods_M - Y_{M,t} = C_{M,t} + I_{M,t} + I_{g,t} + G_t + r_{p,t} K^f_t + r_{p,t}D^f_t - (K^f_{t+1} - K^f_t) - \bigl(D^f_{t+1} - D^f_t\bigr) - RM_t \quad\forall t + Y_{M,t} = C_{M,t} + \pi_{I+1,M}G_t + \pi_{I+2,M}I_{g,t} + I_{M,t} + r_{p,t} K^f_t + r_{p,t}D^f_t - (K^f_{t+1} - K^f_t) - \bigl(D^f_{t+1} - D^f_t\bigr) - RM_t \quad\forall t ``` where ```{math} diff --git a/docs/book/content/theory/stationarization.md b/docs/book/content/theory/stationarization.md index 7f2d0c4b6..dff29498e 100644 --- a/docs/book/content/theory/stationarization.md +++ b/docs/book/content/theory/stationarization.md @@ -244,7 +244,7 @@ The usual definition of equilibrium would be allocations and prices such that ho ```{math} :label: EqStnrzGovBC - e^{g_y}\left(1 + \tilde{g}_{n,t+1}\right)\hat{D}_{t+1} + \hat{Rev}_t + \hat{FA}_t = (1 + r_{gov,t})\hat{D}_t + \hat{G}_t + \hat{I}_{g,t} + \hat{Pensions}_t + \hat{TR}_t + \hat{UBI}_t \quad\forall t + e^{g_y}\left(1 + \tilde{g}_{n,t+1}\right)\hat{D}_{t+1} + \hat{Rev}_t + \hat{FA}_t = (1 + r_{gov,t})\hat{D}_t + p_{g,t}\hat{G}_t + p_{I_g,t}\hat{I}_{g,t} + \hat{Pensions}_t + \hat{TR}_t + \hat{UBI}_t \quad\forall t ``` The stationarized versions of the rule for total government infrastructure investment spending $I_{g,t}$ in {eq}`EqUnbalGBC_Igt` and the rule for government investment spending in each industry in {eq}`EqUnbalGBC_Igt` are found by dividing both sides of the respective equations by $e^{g_y t}\tilde{N}_t$. diff --git a/examples/multi_industry_example.py b/examples/multi_industry_example.py index 92bd34aba..dac086495 100644 --- a/examples/multi_industry_example.py +++ b/examples/multi_industry_example.py @@ -68,7 +68,9 @@ def main(): "debt_ratio_ss": 1.5, "alpha_T": alpha_T.tolist(), "alpha_G": alpha_G.tolist(), - "io_matrix": np.eye(3).tolist(), + "io_matrix": np.vstack( + [np.eye(3), np.array([[0.0, 0.0, 1.0]] * 2)] + ).tolist(), "tau_c": [[0]], } diff --git a/ogcore/SS.py b/ogcore/SS.py index 3966759c1..2bf860aa3 100644 --- a/ogcore/SS.py +++ b/ogcore/SS.py @@ -77,7 +77,7 @@ def euler_equation_solver(guesses, *args): tr, ubi, theta, - p.rho[-1, :], + p.rho[-1, :, j], p.etr_params[-1], p.mtry_params[-1], None, @@ -269,12 +269,12 @@ def inner_loop(outer_loop_vars, p, client): """ # unpack variables to pass to function - bssmat, nssmat, r_p, r, w, p_m, Y, BQ, TR, Ig_baseline, factor = ( + bssmat, nssmat, r_p, r, w, p_m, Y, BQ, G, TR, Ig_baseline, factor = ( outer_loop_vars ) p_m = np.array(p_m) # TODO: why is this a list otherwise? - p_i = np.dot(p.io_matrix, p_m) + p_i, p_g, p_Ig = aggr.get_io_prices(p_m, p, "SS") BQ = np.array(BQ) RM = np.array(aggr.get_RM(Y, p, "SS")) # initialize array for euler errors @@ -461,7 +461,12 @@ def inner_loop(outer_loop_vars, p, client): K_demand_open_vec = np.zeros(p.M) for i_ind in range(p.I): C_vec[i_ind] = aggr.get_C(c_i[i_ind, :, :], p, "SS").item() - Y_vec = np.dot(p.io_matrix.T, C_vec) + I_g = fiscal.get_I_g(Y, Ig_baseline, p, "SS") + Y_vec = ( + np.dot(p.io_matrix[: p.I, :].T, C_vec) + + p.io_matrix[p.I, :] * G + + p.io_matrix[p.I + 1, :] * I_g + ) for m_ind in range(p.M - 1): KYrat_m = firm.get_KY_ratio(r, p_m, p, "SS", m_ind) K_vec[m_ind] = KYrat_m * Y_vec[m_ind] @@ -515,9 +520,7 @@ def inner_loop(outer_loop_vars, p, client): # (S, J) nssmat broadcasts into an (S, S) outer product, scaling the # income sum by S. p.e[-1, :, :] is already (S, J). average_income_model = ( - (new_r_p * b_s + new_w * p.e[-1, :, :] * nssmat) - * p.omega_SS.reshape(p.S, 1) - * p.lambdas.reshape(1, p.J) + (new_r_p * b_s + new_w * p.e[-1, :, :] * nssmat) * p.omega_SS ).sum() if p.baseline: new_factor = p.mean_income_data / average_income_model @@ -533,7 +536,7 @@ def inner_loop(outer_loop_vars, p, client): # Find updated goods prices new_p_m = firm.get_pm(new_w, Y_vec, L_vec, p, "SS") new_p_m = new_p_m / new_p_m[-1] # normalize prices by industry M - new_p_i = np.dot(p.io_matrix, new_p_m) + new_p_i, new_p_g, new_p_Ig = aggr.get_io_prices(new_p_m, p, "SS") new_p_tilde = aggr.get_ptilde(new_p_i, p.tau_c[-1, :], p.alpha_c) num_params = len(p.etr_params[-1][0]) @@ -616,6 +619,8 @@ def inner_loop(outer_loop_vars, p, client): TR, UBI_outlays, I_g, + new_p_g, + new_p_Ig, new_borrowing, debt_service, p, @@ -628,18 +633,18 @@ def inner_loop(outer_loop_vars, p, client): agg_pension_outlays, UBI_outlays, I_g, + new_p_g, + new_p_Ig, p, "SS", ) - G_vec = np.zeros(p.M) - G_vec[-1] = G - C_m_vec = np.dot(p.io_matrix.T, C_vec) + G_vec = p.io_matrix[p.I, :] * G + C_m_vec = np.dot(p.io_matrix[: p.I, :].T, C_vec) I_d_vec = np.zeros(p.M) I_d = aggr.get_I(b_splus1, K_d, K_d, p, "SS") I_d_vec[-1] = I_d - I_g_vec = np.zeros(p.M) - I_g_vec[-1] = I_g + I_g_vec = p.io_matrix[p.I + 1, :] * I_g debt_service_f = fiscal.get_debt_service_f(r_p, D_f) net_capital_outflows = aggr.get_capital_outflows( r_p, K_f, new_borrowing_f, debt_service_f, p @@ -664,6 +669,7 @@ def inner_loop(outer_loop_vars, p, client): Y_vec, new_RM, new_TR, + G, Y, new_factor, new_BQ, @@ -680,6 +686,7 @@ def SS_solver( p_m, Y, BQ, + G, TR, Ig_baseline, factor, @@ -738,6 +745,7 @@ def SS_solver( p_m, Y, BQ, + G, TR, Ig_baseline, factor, @@ -757,6 +765,7 @@ def SS_solver( new_Y_vec, new_RM, new_TR, + new_G, new_Y, new_factor, new_BQ, @@ -772,6 +781,7 @@ def SS_solver( p_m = utils.convex_combo(new_p_m, p_m, nu_ss) factor = utils.convex_combo(new_factor, factor, nu_ss) BQ = utils.convex_combo(new_BQ, BQ, nu_ss) + G = utils.convex_combo(new_G, G, nu_ss) if p.baseline_spending: Y = utils.convex_combo(new_Y, Y, nu_ss) if Y != 0: @@ -781,6 +791,7 @@ def SS_solver( + [utils.pct_diff_func(new_w, w)] + list(utils.pct_diff_func(new_p_m, p_m)) + list(utils.pct_diff_func(new_BQ, BQ)) + + [utils.pct_diff_func(new_G, G)] + [utils.pct_diff_func(new_Y, Y)] + [utils.pct_diff_func(new_factor, factor)] ).max() @@ -793,6 +804,7 @@ def SS_solver( + [utils.pct_diff_func(new_w, w)] + list(utils.pct_diff_func(new_p_m, p_m)) + list(utils.pct_diff_func(new_BQ, BQ)) + + [utils.pct_diff_func(new_G, G)] + [abs(new_Y - Y)] + [utils.pct_diff_func(new_factor, factor)] ).max() @@ -807,6 +819,7 @@ def SS_solver( + [float(utils.pct_diff_func(new_w, w))] + list(utils.pct_diff_func(new_p_m, p_m)) + list(utils.pct_diff_func(new_BQ, BQ)) + + [float(utils.pct_diff_func(new_G, G))] + [float(utils.pct_diff_func(new_TR, TR))] + [float(utils.pct_diff_func(new_factor, factor))] ).max() @@ -833,7 +846,7 @@ def SS_solver( Y_vec_ss = new_Y_vec r_gov_ss = fiscal.get_r_gov(rss, p.debt_ratio_ss, p, "scalar", t=-1) p_m_ss = new_p_m - p_i_ss = np.dot(p.io_matrix, p_m_ss) + p_i_ss, p_g_ss, p_Ig_ss = aggr.get_io_prices(p_m_ss, p, "SS") p_tilde_ss = aggr.get_ptilde(p_i_ss, p.tau_c[-1, :], p.alpha_c) RM_ss = new_RM TR_ss = new_TR @@ -1091,6 +1104,8 @@ def SS_solver( TR_ss, UBI_outlays, I_g_ss, + p_g_ss, + p_Ig_ss, new_borrowing, debt_service, p, @@ -1106,15 +1121,13 @@ def SS_solver( r_p_ss, K_f_ss, new_borrowing_f, debt_service_f, p ) # Fill in arrays, noting that M-1 industries only produce consumption goods - G_vec_ss = np.zeros(p.M) + G_vec_ss = p.io_matrix[p.I, :] * Gss # Map consumption goods back to demands for production goods - logger.info(f"IO: {p.io_matrix.T.shape}, C: {C_vec_ss.shape}") - C_m_vec_ss = np.dot(p.io_matrix.T, C_vec_ss) - G_vec_ss[-1] = Gss + logger.info(f"IO: {p.io_matrix[: p.I, :].T.shape}, C: {C_vec_ss.shape}") + C_m_vec_ss = np.dot(p.io_matrix[: p.I, :].T, C_vec_ss) I_d_vec_ss = np.zeros(p.M) I_d_vec_ss[-1] = I_d_ss - I_g_vec_ss = np.zeros(p.M) - I_g_vec_ss[-1] = I_g_ss + I_g_vec_ss = p.io_matrix[p.I + 1, :] * I_g_ss net_capital_outflows_vec = np.zeros(p.M) net_capital_outflows_vec[-1] = net_capital_outflows RM_vec_ss = np.zeros(p.M) @@ -1184,13 +1197,17 @@ def SS_solver( "total_government_outlays": ( TR_ss + UBI_outlays - + Gss - + I_g_ss + + p_g_ss * Gss + + p_Ig_ss * I_g_ss + debt_service + agg_pension_outlays ), "total_primary_government_outlays": ( - agg_pension_outlays + TR_ss + UBI_outlays + Gss + I_g_ss + agg_pension_outlays + + TR_ss + + UBI_outlays + + p_g_ss * Gss + + p_Ig_ss * I_g_ss ), "G": Gss, "UBI": UBI_outlays, @@ -1215,6 +1232,8 @@ def SS_solver( "w": wss, "p_m": p_m_ss, "p_i": p_i_ss, + "p_g": p_g_ss, + "p_Ig": p_Ig_ss, "p_tilde": p_tilde_ss, "b_sp1": bssmat_splus1, "b_s": bssmat_s, @@ -1275,11 +1294,13 @@ def SS_fsolve(guesses, *args): p_m = guesses[3 : 3 + p.M] Y = guesses[3 + p.M] if p.baseline: - BQ = guesses[3 + p.M + 1 : -2] + BQ = guesses[3 + p.M + 1 : -3] + G = guesses[-3] TR = guesses[-2] factor = guesses[-1] else: - BQ = guesses[3 + p.M + 1 : -1] + BQ = guesses[3 + p.M + 1 : -2] + G = guesses[-2] TR = guesses[-1] factor = factor_ss if p.baseline_spending: @@ -1296,6 +1317,7 @@ def SS_fsolve(guesses, *args): p_m, Y, BQ, + G, TR, Ig_baseline, factor, @@ -1317,6 +1339,7 @@ def SS_fsolve(guesses, *args): new_Y_vec, new_RM, new_TR, + new_G, new_Y, new_factor, new_BQ, @@ -1338,6 +1361,7 @@ def SS_fsolve(guesses, *args): error_Y = float(new_Y - Y) error_BQ = new_BQ - BQ error_TR = float(new_TR - TR) + error_G = float(new_G - G) # divide factor by 1000000 to put on similar scale error_factor = float(new_factor / 1000000 - factor / 1000000) # Check and punish violations of the factor @@ -1349,7 +1373,7 @@ def SS_fsolve(guesses, *args): + list(error_p_m) + [error_Y] + list(error_BQ) - + [error_TR, error_factor] + + [error_G, error_TR, error_factor] ) else: errors = ( @@ -1357,7 +1381,7 @@ def SS_fsolve(guesses, *args): + list(error_p_m) + [error_Y] + list(error_BQ) - + [error_TR] + + [error_G, error_TR] ) error_string = [f"{error:.3e}" for error in errors] logger.info(f"GE loop errors = {error_string}") @@ -1387,6 +1411,7 @@ def SS_initial_guesses(p, b_val=0.0055, n_val=0.4, r_tr_scalars=[1.0, 1.0]): p_m_guess = np.ones(p.M) TRguess = r_tr_scalars[1] * p.initial_guess_TR_SS Yguess = TRguess / p.alpha_T[-1] + Gguess = p.alpha_G[-1] * Yguess # create guesses list # Note that BQ is an vector of lenght J if use_zeta=False @@ -1407,7 +1432,7 @@ def SS_initial_guesses(p, b_val=0.0055, n_val=0.4, r_tr_scalars=[1.0, 1.0]): + list(p_m_guess) + [Yguess] + BQ_items - + [TRguess] + + [Gguess, TRguess] ) if p.baseline: guesses.append(p.initial_guess_factor_SS) @@ -1452,6 +1477,7 @@ def run_SS(p, client=None): wguess, p_m_guess, BQguess, + Gguess, TRguess, Yguess, factor_ss, @@ -1463,6 +1489,7 @@ def run_SS(p, client=None): float(ss_solutions["w"]), ss_solutions["p_m"], ss_solutions["BQ"], + float(ss_solutions["G"]), float(ss_solutions["TR"]), float(ss_solutions["Y"]), ss_solutions["factor"], @@ -1482,7 +1509,7 @@ def run_SS(p, client=None): + list(p_m_guess) + [Yguess] + BQ_items - + [TRguess] + + [Gguess, TRguess] ) # Now solve for the steady state of the reform ss_params = ( @@ -1574,7 +1601,8 @@ def run_SS(p, client=None): wss = sol.x[2] p_m_ss = sol.x[3 : 3 + p.M] if p.baseline: - BQss = sol.x[3 + p.M + 1 : -2] + BQss = sol.x[3 + p.M + 1 : -3] + Gss = sol.x[-3] TR_ss = sol.x[-2] factor_ss = sol.x[-1] Yss = TR_ss / p.alpha_T[-1] # may not be right - if @@ -1582,7 +1610,8 @@ def run_SS(p, client=None): # SS_solver else: Yss = sol.x[3 + p.M] - BQss = sol.x[3 + p.M + 1 : -1] + BQss = sol.x[3 + p.M + 1 : -2] + Gss = sol.x[-2] TR_ss = sol.x[-1] if not p.baseline_spending: Yss = TR_ss / p.alpha_T[-1] # may not be right - if @@ -1605,6 +1634,7 @@ def run_SS(p, client=None): p_m_ss, Yss, BQss, + Gss, TR_ss, Ig_baseline, factor_ss, diff --git a/ogcore/TPI.py b/ogcore/TPI.py index 4dfed4d24..8bbba0864 100644 --- a/ogcore/TPI.py +++ b/ogcore/TPI.py @@ -164,6 +164,13 @@ def get_initial_SS_values(p): B0 = aggr.get_B(ss_baseline_vars["b_sp1"], p, "SS", True) initial_b = ss_baseline_vars["b_sp1"] * (ss_baseline_vars["B"] / B0) initial_n = ss_baseline_vars["n"] + # The DB/NDC/PS pension formulas need the labor supplied before the + # time path begins by cohorts alive at t=0. Use the model's initial + # labor condition (the same baseline object that initializes wealth); + # pre-time-path wages are anchored to the period-0 wage of the + # current path inside pensions.py. See Issue #1014 for a fully + # history-consistent treatment. + p.n_preTP = initial_n Ybaseline = None TRbaseline = None @@ -288,7 +295,7 @@ def firstdoughnutring( np.array([tr]), np.array([ubi]), theta[j], - p.rho[0, -1], + p.rho[0, -1, j], p.etr_params[0][-1], p.mtry_params[0][-1], None, @@ -401,7 +408,7 @@ def twist_doughnut( p_i_s = p_i[t : t + length, :] n_s = n_guess chi_n_s = np.diag(p.chi_n[t : t + p.S, :], max(p.S - length, 0)) - rho_s = np.diag(p.rho[t : t + p.S, :], max(p.S - length, 0)) + rho_s = np.diag(p.rho[t : t + p.S, :, j], max(p.S - length, 0)) error1 = household.FOC_savings( r_s, @@ -517,10 +524,7 @@ def inner_loop(guesses, outer_loop_vars, initial_values, ubi, j, ind, p): r_p, r, w, p_m, BQ, RM, TR, theta = outer_loop_vars # compute composite good price - p_i = ( - np.tile(p.io_matrix.reshape(1, p.I, p.M), (p.T + p.S, 1, 1)) - * np.tile(p_m.reshape(p.T + p.S, 1, p.M), (1, p.I, 1)) - ).sum(axis=2) + p_i, _, _ = aggr.get_io_prices(p_m, p, "TPI") p_tilde = aggr.get_ptilde(p_i[:, :], p.tau_c[:, :], p.alpha_c, "TPI") # compute bq bq = household.get_bq(BQ, None, p, "TPI") @@ -797,10 +801,7 @@ def run_TPI(p, client=None): p_m = p_m / p_m[:, -1].reshape( p.T + p.S, 1 ) # normalize prices by industry M - p_i = ( - np.tile(p.io_matrix.reshape(1, p.I, p.M), (p.T + p.S, 1, 1)) - * np.tile(p_m.reshape(p.T + p.S, 1, p.M), (1, p.I, 1)) - ).sum(axis=2) + p_i, p_g, p_Ig = aggr.get_io_prices(p_m, p, "TPI") p_tilde = aggr.get_ptilde(p_i[:, :], p.tau_c[:, :], p.alpha_c, "TPI") if not any(p.zeta_K == 1): w[: p.T] = np.squeeze( @@ -813,10 +814,7 @@ def run_TPI(p, client=None): p_m = p_m / p_m[:, -1].reshape( p.T + p.S, 1 ) # normalize prices by industry M - p_i = ( - np.tile(p.io_matrix.reshape(1, p.I, p.M), (p.T + p.S, 1, 1)) - * np.tile(p_m.reshape(p.T + p.S, 1, p.M), (1, p.I, 1)) - ).sum(axis=2) + p_i, p_g, p_Ig = aggr.get_io_prices(p_m, p, "TPI") p_tilde = aggr.get_ptilde(p_i[:, :], p.tau_c[:, :], p.alpha_c, "TPI") # path for interest rates r = np.zeros_like(Y) @@ -970,10 +968,7 @@ def run_TPI(p, client=None): while (TPIiter < p.maxiter) and (TPIdist >= p.mindist_TPI): outer_loop_vars = (r_p, r, w, p_m, BQ, RM, TR, theta) # compute composite good price - p_i = ( - np.tile(p.io_matrix.reshape(1, p.I, p.M), (p.T + p.S, 1, 1)) - * np.tile(p_m.reshape(p.T + p.S, 1, p.M), (1, p.I, 1)) - ).sum(axis=2) + p_i, p_g, p_Ig = aggr.get_io_prices(p_m, p, "TPI") p_tilde = aggr.get_ptilde(p_i[:, :], p.tau_c[:, :], p.alpha_c, "TPI") # Initialize Euler errors @@ -1183,9 +1178,11 @@ def run_TPI(p, client=None): for i_ind in range(p.I): C_vec[:, i_ind] = aggr.get_C(c_i[: p.T, i_ind, :, :], p, "TPI") Y_vec = ( - np.tile(p.io_matrix.reshape(1, p.I, p.M), (p.T, 1, 1)) + np.tile(p.io_matrix[: p.I, :].reshape(1, p.I, p.M), (p.T, 1, 1)) * np.tile(C_vec[: p.T, :].reshape(p.T, p.I, 1), (1, 1, p.M)) ).sum(axis=1) + Y_vec += G[: p.T, None] * p.io_matrix[p.I, :] + Y_vec += I_g[: p.T, None] * p.io_matrix[p.I + 1, :] for m_ind in range(p.M - 1): KYrat_m = firm.get_KY_ratio( r[: p.T], p_m[: p.T, :], p, "TPI", m_ind @@ -1272,6 +1269,8 @@ def run_TPI(p, client=None): UBI_outlays, TR, I_g, + p_g, + p_Ig, Gbaseline, D0_baseline, ) @@ -1332,6 +1331,7 @@ def run_TPI(p, client=None): new_p_m = new_p_m / new_p_m[:, -1].reshape( p.T, 1 ) # normalize prices by industry M + _, new_p_g, new_p_Ig = aggr.get_io_prices(new_p_m, p, "TPI") b_mat_shift = np.append( np.reshape(initial_b, (1, p.S, p.J)), @@ -1380,6 +1380,8 @@ def run_TPI(p, client=None): agg_pension_outlays[: p.T], UBI_outlays[: p.T], I_g[: p.T], + new_p_g[: p.T], + new_p_Ig[: p.T], p, "TPI", ) @@ -1625,18 +1627,16 @@ def run_TPI(p, client=None): debt_service_f[: p.T], p, ) - # Fill in arrays, noting that M-1 industries only produce consumption goods - G_vec = np.zeros((p.T, p.M)) - G_vec[:, -1] = G[: p.T] + # Map government composite quantities into their industry inputs. + G_vec = G[: p.T, None] * p.io_matrix[p.I, :] # Map consumption goods back to demands for production goods C_m_vec = ( - np.tile(p.io_matrix.reshape(1, p.I, p.M), (p.T, 1, 1)) + np.tile(p.io_matrix[: p.I, :].reshape(1, p.I, p.M), (p.T, 1, 1)) * np.tile(C_vec[: p.T, :].reshape(p.T, p.I, 1), (1, 1, p.M)) ).sum(axis=1) I_d_vec = np.zeros((p.T, p.M)) I_d_vec[:, -1] = I_d[: p.T] - I_g_vec = np.zeros((p.T, p.M)) - I_g_vec[:, -1] = I_g[: p.T] + I_g_vec = I_g[: p.T, None] * p.io_matrix[p.I + 1, :] net_capital_outflows_vec = np.zeros((p.T, p.M)) net_capital_outflows_vec[:, -1] = net_capital_outflows[: p.T] RM_vec = np.zeros((p.T, p.M)) @@ -1706,8 +1706,8 @@ def run_TPI(p, client=None): "total_government_outlays": ( TR[: p.T, ...] + UBI[: p.T, ...] - + G[: p.T, ...] - + I_g[: p.T, ...] + + p_g[: p.T, ...] * G[: p.T, ...] + + p_Ig[: p.T, ...] * I_g[: p.T, ...] + debt_service[: p.T, ...] + agg_pension_outlays[: p.T, ...] ), @@ -1715,8 +1715,8 @@ def run_TPI(p, client=None): agg_pension_outlays[: p.T, ...] + TR[: p.T, ...] + UBI[: p.T, ...] - + G[: p.T, ...] - + I_g[: p.T, ...] + + p_g[: p.T, ...] * G[: p.T, ...] + + p_Ig[: p.T, ...] * I_g[: p.T, ...] ), "total_tax_revenue": total_tax_revenue[: p.T, ...], "business_tax_revenue": business_tax_revenue[: p.T, ...], @@ -1739,6 +1739,8 @@ def run_TPI(p, client=None): "w": w[: p.T, ...], "p_m": p_m[: p.T, ...], "p_i": p_i[: p.T, ...], + "p_g": p_g[: p.T, ...], + "p_Ig": p_Ig[: p.T, ...], "p_tilde": p_tilde[: p.T, ...], "b_sp1": bmat_splus1[: p.T, ...], "b_s": bmat_s[: p.T, ...], diff --git a/ogcore/__init__.py b/ogcore/__init__.py index 5a471f5e0..40b3bba0f 100644 --- a/ogcore/__init__.py +++ b/ogcore/__init__.py @@ -21,4 +21,4 @@ from ogcore.txfunc import * # noqa: F403 from ogcore.utils import * # noqa: F403 -__version__ = "0.16.4" +__version__ = "0.19.0" diff --git a/ogcore/aggregates.py b/ogcore/aggregates.py index 6d0d69542..7753f97e1 100644 --- a/ogcore/aggregates.py +++ b/ogcore/aggregates.py @@ -15,6 +15,17 @@ """ +def get_io_prices(p_m, p, method): + """Map industry prices into household and government-good prices.""" + if method == "SS": + prices = np.dot(p.io_matrix, p_m) + return prices[: p.I], prices[p.I], prices[p.I + 1] + if method == "TPI": + prices = np.einsum("im,tm->ti", p.io_matrix, p_m) + return prices[:, : p.I], prices[:, p.I], prices[:, p.I + 1] + raise ValueError("method must be 'SS' or 'TPI'") + + def get_L(n, p, method): r""" Calculate aggregate labor supply. @@ -37,12 +48,10 @@ def get_L(n, p, method): # the J axis to a 1-D array, and the subsequent multiplication against # the (S, J) weight broadcasts into an (S, S) outer product, producing # a labor aggregate S times too large. p.e[-1, :, :] is already (S, J). - L_presum = p.e[-1, :, :] * np.transpose(p.omega_SS * p.lambdas) * n + L_presum = p.e[-1, :, :] * p.omega_SS * n L = L_presum.sum() elif method == "TPI": - L_presum = (n * (p.e * np.squeeze(p.lambdas))) * np.tile( - np.reshape(p.omega[: p.T, :], (p.T, p.S, 1)), (1, 1, p.J) - ) + L_presum = n * (p.e * p.omega[: p.T, :, :]) L = L_presum.sum(1).sum(1) return L @@ -69,33 +78,28 @@ def get_I(b_splus1, K_p1, K, p, method): """ if method == "SS": - omega_extended = np.append(p.omega_SS[1:], [0.0]) - imm_extended = np.append(p.imm_rates[-1, 1:], [0.0]) - part2 = ( - ( - b_splus1 - * np.transpose((omega_extended * imm_extended) * p.lambdas) - ).sum() - ) / (1 + p.g_n_ss) + omega_extended = np.append( + p.omega_SS[1:, :], np.zeros((1, p.J)), axis=0 + ) + imm_extended = np.append( + p.imm_rates[-1, 1:, :], np.zeros((1, p.J)), axis=0 + ) + part2 = ((b_splus1 * omega_extended * imm_extended).sum()) / ( + 1 + p.g_n_ss + ) aggI = (1 + p.g_n_ss) * np.exp(p.g_y) * (K_p1 - part2) - ( 1.0 - p.delta ) * K elif method == "TPI": - omega_shift = np.append(p.omega[: p.T, 1:], np.zeros((p.T, 1)), axis=1) + omega_shift = np.append( + p.omega[: p.T, 1:, :], np.zeros((p.T, 1, p.J)), axis=1 + ) imm_shift = np.append( - p.imm_rates[: p.T, 1:], np.zeros((p.T, 1)), axis=1 + p.imm_rates[: p.T, 1:, :], np.zeros((p.T, 1, p.J)), axis=1 + ) + part2 = ((b_splus1 * imm_shift * omega_shift).sum(1).sum(1)) / ( + 1 + np.squeeze(np.hstack((p.g_n[: p.T - 1], p.g_n_ss))) ) - part2 = ( - ( - (b_splus1 * np.squeeze(p.lambdas)) - * np.tile( - np.reshape(imm_shift * omega_shift, (p.T, p.S, 1)), - (1, 1, p.J), - ) - ) - .sum(1) - .sum(1) - ) / (1 + np.squeeze(np.hstack((p.g_n[: p.T - 1], p.g_n_ss)))) aggI = ( 1 + np.squeeze(np.hstack((p.g_n[: p.T - 1], p.g_n_ss))) ) * np.exp(p.g_y) * (K_p1 - part2) - (1.0 - p.delta) * K @@ -129,30 +133,36 @@ def get_B(b, p, method, preTP): """ if method == "SS": if preTP: - part1 = b * np.transpose(p.omega_S_preTP * p.lambdas) - omega_extended = np.append(p.omega_S_preTP[1:], [0.0]) - imm_extended = np.append(p.imm_rates_preTP[1:], [0.0]) + part1 = b * p.omega_S_preTP + omega_extended = np.append( + p.omega_S_preTP[1:, :], np.zeros((1, p.J)), axis=0 + ) + imm_extended = np.append( + p.imm_rates_preTP[1:, :], np.zeros((1, p.J)), axis=0 + ) pop_growth_rate = p.g_n_preTP else: - part1 = b * np.transpose(p.omega_SS * p.lambdas) - omega_extended = np.append(p.omega_SS[1:], [0.0]) - imm_extended = np.append(p.imm_rates[-1, 1:], [0.0]) + part1 = b * p.omega_SS + omega_extended = np.append( + p.omega_SS[1:, :], np.zeros((1, p.J)), axis=0 + ) + imm_extended = np.append( + p.imm_rates[-1, 1:, :], np.zeros((1, p.J)), axis=0 + ) pop_growth_rate = p.g_n_ss - part2 = b * np.transpose(omega_extended * imm_extended * p.lambdas) + part2 = b * omega_extended * imm_extended B_presum = part1 + part2 B = B_presum.sum() B /= 1.0 + pop_growth_rate elif method == "TPI": - part1 = (b * np.squeeze(p.lambdas)) * np.tile( - np.reshape(p.omega[: p.T, :], (p.T, p.S, 1)), (1, 1, p.J) + part1 = b * p.omega[: p.T, :, :] + omega_shift = np.append( + p.omega[: p.T, 1:, :], np.zeros((p.T, 1, p.J)), axis=1 ) - omega_shift = np.append(p.omega[: p.T, 1:], np.zeros((p.T, 1)), axis=1) imm_shift = np.append( - p.imm_rates[: p.T, 1:], np.zeros((p.T, 1)), axis=1 - ) - part2 = (b * np.squeeze(p.lambdas)) * np.tile( - np.reshape(imm_shift * omega_shift, (p.T, p.S, 1)), (1, 1, p.J) + p.imm_rates[: p.T, 1:, :], np.zeros((p.T, 1, p.J)), axis=1 ) + part2 = b * omega_shift * imm_shift B_presum = part1 + part2 B = B_presum.sum(1).sum(1) B /= 1.0 + np.hstack((p.g_n[: p.T - 1], p.g_n_ss)) @@ -194,27 +204,27 @@ def get_BQ(r, b_splus1, j, p, method, preTP): pop_growth_rate = p.g_n_ss rho = p.rho[-1, :] if j is not None: - BQ_presum = omega * rho * b_splus1 * p.lambdas[j] + BQ_presum = omega[:, j] * rho[:, j] * b_splus1 else: - BQ_presum = np.transpose(omega * (rho * p.lambdas)) * b_splus1 + BQ_presum = omega * rho * b_splus1 BQ = BQ_presum.sum(0) BQ *= (1.0 + r) / (1.0 + pop_growth_rate) elif method == "TPI": pop = np.append( - p.omega_S_preTP.reshape(1, p.S), p.omega[: p.T - 1, :], axis=0 + p.omega_S_preTP.reshape(1, p.S, p.J), + p.omega[: p.T - 1, :, :], + axis=0, ) rho = np.append( - p.rho_preTP.reshape(1, p.S), p.rho[: p.T - 1, :], axis=0 + p.rho_preTP.reshape(1, p.S, p.J), p.rho[: p.T - 1, :, :], axis=0 ) if j is not None: - BQ_presum = (b_splus1 * p.lambdas[j]) * (pop * rho) + BQ_presum = b_splus1 * pop[:, :, j] * rho[:, :, j] BQ = BQ_presum.sum(1) BQ *= (1.0 + r) / (1.0 + np.append(p.g_n_preTP, p.g_n[: p.T - 1])) else: - BQ_presum = (b_splus1 * np.squeeze(p.lambdas)) * np.tile( - np.reshape(pop * rho, (p.T, p.S, 1)), (1, 1, p.J) - ) + BQ_presum = b_splus1 * pop * rho BQ = BQ_presum.sum(1) BQ *= np.tile( np.reshape( @@ -302,22 +312,9 @@ def get_C(c, p, method): """ if method == "SS": - aggC = ( - (c * np.transpose(p.omega_SS * p.lambdas).reshape(1, p.S, p.J)) - .sum(-1) - .sum(-1) - ) + aggC = (c * p.omega_SS).sum(-1).sum(-1) elif method == "TPI": - aggC = ( - ( - (c * np.squeeze(p.lambdas)) - * np.tile( - np.reshape(p.omega[: p.T, :], (p.T, p.S, 1)), (1, 1, p.J) - ) - ) - .sum(-1) - .sum(-1) - ) + aggC = (c * p.omega[: p.T, :, :]).sum(-1).sum(-1) return aggC @@ -403,8 +400,8 @@ def revenue( bq_tax_liab = tax.bequest_tax_liab(r, b, bq, 0, None, method, p) w_tax_liab = tax.wealth_tax_liab(r, b, 0, None, method, p) if method == "SS": - p_i = np.dot(p.io_matrix, p_m) - pop_weights = np.transpose(p.omega_SS * p.lambdas) + p_i, _, _ = get_io_prices(p_m, p, "SS") + pop_weights = p.omega_SS iit_payroll_tax_revenue = (inc_pay_tax_liab * pop_weights).sum() agg_pension_outlays = (pension_benefits * pop_weights).sum() UBI_outlays = (ubi * pop_weights).sum() @@ -415,13 +412,8 @@ def revenue( ).sum() payroll_tax_revenue = p.frac_tax_payroll[-1] * iit_payroll_tax_revenue elif method == "TPI": - p_i = ( - np.tile(p.io_matrix.reshape(1, p.I, p.M), (p.T, 1, 1)) - * np.tile(p_m[: p.T, :].reshape(p.T, 1, p.M), (1, p.I, 1)) - ).sum(axis=2) - pop_weights = np.squeeze(p.lambdas) * np.tile( - np.reshape(p.omega[: p.T, :], (p.T, p.S, 1)), (1, 1, p.J) - ) + p_i, _, _ = get_io_prices(p_m[: p.T, :], p, "TPI") + pop_weights = p.omega[: p.T, :, :] iit_payroll_tax_revenue = ( (inc_pay_tax_liab * pop_weights).sum(1).sum(1) ) diff --git a/ogcore/default_parameters.json b/ogcore/default_parameters.json index 9055bfa96..cf2ccefdf 100644 --- a/ogcore/default_parameters.json +++ b/ogcore/default_parameters.json @@ -528,7 +528,7 @@ }, "io_matrix": { "title": "Input-output matrix", - "description": "Input-output matrix used to map production outputs into consumption goods using a fixed coefficient model. This matrix has dimensions I x M, where I is the number of distinct consumption goods and M is the number of distinct production goods. The sum each row of this matrix must be 1.", + "description": "Input-output matrix used to map production outputs into consumption and government goods using a fixed coefficient model. This matrix has dimensions (I + 2) x M. The first I rows map consumption goods, row I maps government consumption, and row I + 1 maps infrastructure investment. The sum of each row must be 1.", "short_description": "Input-output matrix", "param_notation": "$\\Pi^I$", "section_1": "Firm Parameters", @@ -540,6 +540,10 @@ { "value": [[ 1.0 + ], [ + 1.0 + ], [ + 1.0 ]] } ], @@ -4079,6 +4083,50 @@ } } }, + "ndc_growth_rate": { + "title": "Growth rate applied to contributions under a notional defined contribution system.", + "description": "Growth rate applied to contributions under a notional defined contribution system.", + "section_1": "Fiscal Policy Parameters", + "section_2": "Government Pension Parameters", + "notes": "'r' uses the model interest rate, 'Curr GDP' the current GDP growth rate, and 'LR GDP' the long-run GDP growth rate.", + "type": "str", + "value": [ + { + "value": "LR GDP" + } + ], + "validators": { + "choice": { + "choices": [ + "r", + "Curr GDP", + "LR GDP" + ] + } + } + }, + "dir_growth_rate": { + "title": "Growth rate used in the conversion coefficient of a notional defined contribution system.", + "description": "Growth rate used in the conversion coefficient of a notional defined contribution system.", + "section_1": "Fiscal Policy Parameters", + "section_2": "Government Pension Parameters", + "notes": "'r' uses the model interest rate, 'Curr GDP' the current GDP growth rate, and 'LR GDP' the long-run GDP growth rate.", + "type": "str", + "value": [ + { + "value": "LR GDP" + } + ], + "validators": { + "choice": { + "choices": [ + "r", + "Curr GDP", + "LR GDP" + ] + } + } + }, "alpha_db": { "title": "Replacement rate under a defined contribution system.", "description": "Replacement rate under a defined contribution system.", @@ -4117,6 +4165,28 @@ } } }, + "points_growth_rate": { + "title": "Growth rate applied to contributions under a points system pension.", + "description": "Growth rate applied to contributions under a points system pension.", + "section_1": "Fiscal Policy Parameters", + "section_2": "Government Pension Parameters", + "notes": "'r' uses the model interest rate, 'Curr GDP' the current GDP growth rate, and 'LR GDP' the long-run GDP growth rate.", + "type": "str", + "value": [ + { + "value": "LR GDP" + } + ], + "validators": { + "choice": { + "choices": [ + "r", + "Curr GDP", + "LR GDP" + ] + } + } + }, "yr_contrib": { "title": "Number of years of contributions made to defined benefits pension system.", "description": "Number of years of contributions made to defined benefits pension system.", @@ -4897,231 +4967,580175 @@ "section_1": "Demographic Parameters", "notes": "", "type": "float", - "number_dims": 2, + "number_dims": 3, "value": [ { "value": [ - [0.017156283268392736, 0.017452093807687842, 0.017235053688839676, 0.017301066782198594, 0.017405943241341485, 0.017598594820840113, 0.01801609065517206, 0.01839673988912017, 0.018690382571279363, 0.01916932122269548, 0.01946450678567942, 0.019354551957155908, 0.01855428858205526, 0.01807685602060747, 0.01773216627333844, 0.01776239626885281, 0.017835394707773487, 0.017254452913899737, 0.017480633103504728, 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0.28919518, + 0.28919518, + 0.28919518, + 0.28919518, + 0.28919518], + [1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0]] } ], "validators": { diff --git a/ogcore/demographics.py b/ogcore/demographics.py index 40c37174a..86a85db46 100644 --- a/ogcore/demographics.py +++ b/ogcore/demographics.py @@ -672,6 +672,423 @@ def immsolve(imm_rates, *args): return omega_errs +def _logistic(x): + """ + Numerically stable logistic transform. + """ + return 1 / (1 + np.exp(-np.clip(x, -700, 700))) + + +def _income_shares_and_midpoints(income_percentiles): + """ + Convert income group population shares into centered percentile midpoints. + """ + income_shares = np.asarray(income_percentiles, dtype=float).ravel() + if income_shares.ndim != 1 or income_shares.size < 1: + raise ValueError("income_percentiles must be a one-dimensional array.") + if np.any(income_shares <= 0): + raise ValueError("income_percentiles must contain positive values.") + income_shares = income_shares / income_shares.sum() + percentile_midpoints = ( + 100 * (np.cumsum(income_shares) - 0.5 * income_shares) - 50 + ) + return income_shares, percentile_midpoints + + +def _extend_time_path(arr, num_periods): + """ + Extend or trim the first dimension of an array to num_periods. + """ + if arr.shape[0] == num_periods: + return arr + if arr.shape[0] > num_periods: + return arr[:num_periods] + extension = np.repeat(arr[-1:, ...], num_periods - arr.shape[0], axis=0) + return np.concatenate((arr, extension), axis=0) + + +def _format_age_gradient(gradient, num_periods, E, S, name): + """ + Put an age gradient into a num_periods x (E+S) array. + + A length-S vector is interpreted as applying to economically active ages + only, with zero gradients for younger ages. + """ + totpers = E + S + if gradient is None: + return np.zeros((num_periods, totpers)) + + gradient = np.asarray(gradient, dtype=float) + if gradient.ndim == 0: + return np.full((num_periods, totpers), gradient) + + if gradient.ndim == 1: + if gradient.shape[0] == 1: + full_gradient = np.full(totpers, gradient.item()) + elif gradient.shape[0] == S: + full_gradient = np.concatenate((np.zeros(E), gradient)) + elif gradient.shape[0] == totpers: + full_gradient = gradient + else: + raise ValueError( + f"{name} must have length 1, S={S}, or E+S={totpers}." + ) + return np.tile(full_gradient.reshape(1, totpers), (num_periods, 1)) + + if gradient.ndim == 2: + if gradient.shape[1] == S: + gradient = np.concatenate( + (np.zeros((gradient.shape[0], E)), gradient), axis=1 + ) + elif gradient.shape[1] != totpers: + raise ValueError( + f"{name} must have second dimension S={S} or E+S={totpers}." + ) + return _extend_time_path(gradient, num_periods) + + raise ValueError(f"{name} must be a scalar, vector, or 2D array.") + + +def _format_infmort_gradient(gradient, num_periods): + """ + Put an infant mortality gradient into a num_periods vector. + """ + if gradient is None: + return np.zeros(num_periods) + + gradient = np.asarray(gradient, dtype=float) + if gradient.ndim == 0: + return np.full(num_periods, gradient) + if gradient.ndim == 1: + if gradient.shape[0] == 1: + return np.full(num_periods, gradient.item()) + return _extend_time_path(gradient.reshape(-1, 1), num_periods).ravel() + + raise ValueError("infmort_gradient must be a scalar or vector.") + + +def _format_imm_shares(imm_pctiles, num_periods, E, S, income_shares): + """ + Format immigrant income shares as num_periods x (E+S) x J. + """ + totpers = E + S + J = income_shares.shape[0] + if imm_pctiles is None: + return np.tile( + income_shares.reshape(1, 1, J), (num_periods, totpers, 1) + ) + + imm_shares = np.asarray(imm_pctiles, dtype=float) + if imm_shares.ndim == 1: + if imm_shares.shape[0] != J: + raise ValueError(f"imm_pctiles must have J={J} elements.") + imm_shares = np.tile( + imm_shares.reshape(1, 1, J), (num_periods, totpers, 1) + ) + elif imm_shares.ndim == 2: + if imm_shares.shape[-1] != J: + raise ValueError(f"imm_pctiles last dimension must be J={J}.") + if imm_shares.shape[0] == S: + young_shares = np.tile(income_shares.reshape(1, J), (E, 1)) + imm_shares = np.concatenate((young_shares, imm_shares), axis=0) + elif imm_shares.shape[0] != totpers: + raise ValueError( + f"imm_pctiles first dimension must be S={S} or E+S={totpers}." + ) + imm_shares = np.tile( + imm_shares.reshape(1, totpers, J), (num_periods, 1, 1) + ) + elif imm_shares.ndim == 3: + if imm_shares.shape[-1] != J: + raise ValueError(f"imm_pctiles last dimension must be J={J}.") + if imm_shares.shape[1] == S: + young_shares = np.tile( + income_shares.reshape(1, 1, J), (imm_shares.shape[0], E, 1) + ) + imm_shares = np.concatenate((young_shares, imm_shares), axis=1) + elif imm_shares.shape[1] != totpers: + raise ValueError( + f"imm_pctiles second dimension must be S={S} or E+S={totpers}." + ) + imm_shares = _extend_time_path(imm_shares, num_periods) + else: + raise ValueError( + "imm_pctiles must be a vector, 2D array, or 3D array." + ) + + if np.any(imm_shares < 0): + raise ValueError("imm_pctiles must be nonnegative.") + denom = imm_shares.sum(axis=-1, keepdims=True) + if np.any(denom <= 0): + raise ValueError("imm_pctiles must sum to a positive value across J.") + return imm_shares / denom + + +def _within_age_weights(pop_by_age_j, income_shares): + """ + Compute J weights within each age, using income_shares if an age is empty. + """ + age_totals = pop_by_age_j.sum(axis=-1, keepdims=True) + default_weights = np.tile( + income_shares.reshape(1, income_shares.shape[0]), + (pop_by_age_j.shape[0], 1), + ) + return np.divide( + pop_by_age_j, + age_totals, + out=default_weights, + where=age_totals > 0, + ) + + +def _mean_preserving_logit_rates(mean_rates, slopes, weights, pct_midpoints): + """ + Create J-specific rates bounded in [0, 1] with weighted mean mean_rates. + """ + mean_rates = np.asarray(mean_rates, dtype=float) + slopes = np.asarray(slopes, dtype=float) + weights = np.asarray(weights, dtype=float) + rates = np.zeros(weights.shape) + + for idx in np.ndindex(mean_rates.shape): + mean_rate = mean_rates[idx] + slope = slopes[idx] + w = weights[idx] + w = w / w.sum() + + if mean_rate <= 0: + rates[idx] = 0.0 + elif mean_rate >= 1: + rates[idx] = 1.0 + elif np.isclose(slope, 0.0): + rates[idx] = mean_rate + else: + + def mean_error(intercept): + return ( + np.dot(w, _logistic(intercept + slope * pct_midpoints)) + - mean_rate + ) + + intercept = opt.brentq(mean_error, -700, 700) + rates[idx] = _logistic(intercept + slope * pct_midpoints) + + return rates + + +def expand_pop_obj_J( + omega_path_lev, + omega_path_S, + omega_SSfx, + fert_rates, + mort_rates, + infmort_rates, + imm_rates, + mort_rates_S, + imm_rates_mat, + E, + S, + g_n_SS, + fixper, + income_percentiles=None, + fert_gradient=None, + mort_gradient=None, + infmort_gradient=None, + imm_pctiles=None, +): + """ + Expand aggregate demographic objects to age x income-group objects. + + The aggregate population path and rates are left unchanged. If + income_percentiles is None and no income-specific inputs are + provided, the aggregate objects are broadcast across a single + income group (J=1). Otherwise, income_percentiles gives the initial + population distribution across J for every age and the income + shares of newborns in every period. + + Args: + omega_path_lev (Numpy array): T+S x E+S aggregate population levels. + omega_path_S (Numpy array): T+S x S aggregate active-age population + shares. + omega_SSfx (Numpy array): fixed full-life population distribution. + fert_rates (Numpy array): T+S x E+S fertility rates. + mort_rates (Numpy array): T+S x E+S mortality rates. + infmort_rates (Numpy array): T+S infant mortality rates. + imm_rates (Numpy array): T+S x E+S immigration rates, including the + adjusted post-fixper rates. + mort_rates_S (Numpy array): T+S x S mortality rates for active ages. + imm_rates_mat (Numpy array): T+S x S immigration rates for active ages. + E (int): number of non-economically active periods. + S (int): number of economically active periods. + g_n_SS (float): steady-state population growth rate. + fixper (int): period at which the fixed steady-state distribution is + imposed. + income_percentiles (array_like): population shares for each J + group; defaults to a single income group when no + income-specific inputs are supplied. + fert_gradient (array_like): log-odds fertility slopes by age. + mort_gradient (array_like): log-odds mortality slopes by age. + infmort_gradient (array_like): log-odds infant mortality slopes. + imm_pctiles (array_like): immigrant income shares by period, + age, and J. + + Returns: + dict: demographic objects with the same keys needed by get_pop_objs. + """ + omega_SS = omega_SSfx[-S:] / omega_SSfx[-S:].sum() + income_inputs = ( + fert_gradient, + mort_gradient, + infmort_gradient, + imm_pctiles, + ) + all_income_inputs_none = all(x is None for x in income_inputs) + if income_percentiles is None: + assert all_income_inputs_none, ( + "income_percentiles must be provided when using " + + "income-specific inputs." + ) + # No income heterogeneity requested: broadcast the aggregate + # objects across a single income group. + income_percentiles = [100] + income_shares, pct_midpoints = _income_shares_and_midpoints( + income_percentiles + ) + J = income_shares.shape[0] + num_periods, totpers = omega_path_lev.shape + assert totpers == E + S + + if all_income_inputs_none: + return { + "omega_path_S": omega_path_S.reshape( + omega_path_S.shape[0], omega_path_S.shape[1], 1 + ) + * income_shares.reshape(1, 1, J), + "omega_SS": omega_SS.reshape(omega_SS.shape[0], 1) + * income_shares.reshape(1, J), + "mort_rates_S": np.tile( + mort_rates_S.reshape( + mort_rates_S.shape[0], mort_rates_S.shape[1], 1 + ), + (1, 1, J), + ), + "imm_rates_mat": np.tile( + imm_rates_mat.reshape( + imm_rates_mat.shape[0], imm_rates_mat.shape[1], 1 + ), + (1, 1, J), + ), + } + + fert_slopes = _format_age_gradient( + fert_gradient, num_periods, E, S, "fert_gradient" + ) + mort_slopes = _format_age_gradient( + mort_gradient, num_periods, E, S, "mort_gradient" + ) + infmort_slopes = _format_infmort_gradient(infmort_gradient, num_periods) + imm_shares = _format_imm_shares( + imm_pctiles, num_periods, E, S, income_shares + ) + + # Use the aggregate fixed distribution after fixper, matching the + # age-only steady-state logic already computed above. + target_pop = np.array(omega_path_lev, dtype=float, copy=True) + fixed_full_dist = omega_SSfx / omega_SSfx.sum() + if fixper < num_periods: + total_pop = target_pop[fixper].sum() + target_pop[fixper] = total_pop * fixed_full_dist + for t in range(fixper + 1, num_periods): + total_pop *= 1 + g_n_SS + target_pop[t] = total_pop * fixed_full_dist + + pop_path_J = np.zeros((num_periods, totpers, J)) + fert_rates_J = np.zeros((num_periods, totpers, J)) + mort_rates_J = np.zeros((num_periods, totpers, J)) + imm_rates_J = np.zeros((num_periods, totpers, J)) + infmort_rates_J = np.zeros((num_periods, J)) + pop_path_J[0] = target_pop[0, :, None] * income_shares.reshape(1, J) + fixed_pop_dist_J = None + + for t in range(num_periods): + pop_t_J = pop_path_J[t] + age_weights = _within_age_weights(pop_t_J, income_shares) + fert_rates_J[t] = _mean_preserving_logit_rates( + fert_rates[t], fert_slopes[t], age_weights, pct_midpoints + ) + mort_rates_J[t] = _mean_preserving_logit_rates( + mort_rates[t], mort_slopes[t], age_weights, pct_midpoints + ) + infmort_rates_J[t] = _mean_preserving_logit_rates( + np.array([infmort_rates[t]]), + np.array([infmort_slopes[t]]), + income_shares.reshape(1, J), + pct_midpoints, + )[0] + + births = (fert_rates_J[t] * pop_t_J).sum() + newborns = births * income_shares + pre_imm_pop = np.zeros((totpers, J)) + pre_imm_pop[0] = (1 - infmort_rates_J[t]) * newborns + pre_imm_pop[1:] = pop_t_J[:-1] * (1 - mort_rates_J[t, :-1]) + + if t + 1 < num_periods: + target_next = target_pop[t + 1] + else: + newborns_agg = np.dot(fert_rates[t], target_pop[t]) + target_next = np.zeros(totpers) + target_next[0] = (1 - infmort_rates[t]) * newborns_agg + imm_rates[ + t, 0 + ] * target_pop[t, 0] + target_next[1:] = ( + target_pop[t, :-1] * (1 - mort_rates[t, :-1]) + + imm_rates[t, 1:] * target_pop[t, 1:] + ) + + if t == fixper: + fixed_pop_dist_J = pop_t_J / pop_t_J.sum() + + if fixed_pop_dist_J is not None and t >= fixper: + target_next_J = fixed_pop_dist_J * target_next.sum() + imm_flow_J = target_next_J - pre_imm_pop + pop_next_J = target_next_J + else: + imm_flow = target_next - pre_imm_pop.sum(axis=1) + imm_flow_J = imm_flow[:, None] * imm_shares[t] + pop_next_J = pre_imm_pop + imm_flow_J + + imm_rates_J[t] = np.divide( + imm_flow_J, + pop_t_J, + out=np.zeros_like(imm_flow_J), + where=pop_t_J != 0, + ) + + if t + 1 < num_periods: + if np.any(pop_next_J < -1e-8): + raise ValueError( + "Income-specific demographic inputs imply a negative " + "population in at least one age-income cell." + ) + pop_path_J[t + 1] = np.maximum(pop_next_J, 0.0) + + active_pop_J = pop_path_J[:, E:, :] + omega_path_S_J = active_pop_J / active_pop_J.sum(axis=(1, 2)).reshape( + num_periods, 1, 1 + ) + omega_SS_J = omega_path_S_J[fixper] + + assert np.allclose(omega_path_S_J.sum(axis=2), omega_path_S) + assert np.allclose(omega_SS_J.sum(axis=1), omega_SS) + + return { + "omega_path_S": omega_path_S_J, + "omega_SS": omega_SS_J, + "mort_rates_S": mort_rates_J[:, E:, :], + "imm_rates_mat": imm_rates_J[:, E:, :], + } + + def get_pop_objs( E=20, S=80, @@ -684,6 +1101,11 @@ def get_pop_objs( imm_rates=None, infer_pop=False, pop_dist=None, + fert_gradient=None, + mort_gradient=None, + infmort_gradient=None, + imm_pctiles=None, + income_percentiles=None, country_id=UN_COUNTRY_CODE, initial_data_year=START_YEAR - 1, final_data_year=START_YEAR + 2, @@ -714,6 +1136,25 @@ def get_pop_objs( infer_pop (bool): =True if want to infer the population pop_dist (array_like): user provided population distribution, dimensions are T0+1 x E+S + fert_gradient (array_like): user provided fertility rate gradient, + dimensions are S, represents the log-odds slope in the + fertility rate + per percentile of the lifetime income distribution. + mort_gradient (array_like): user provided mortality rate gradient, + dimensions are S, represents the log-odds slope in the + mortality rate + per percentile of the lifetime income distribution. + infmort_gradient (array_like): user provided infant mortality + rate gradient, dimensions are S, represents the log-odds + slope in the infant mortality rate per percentile of the + lifetime income distribution. + imm_pctiles (array_like): user provided lifetime income distribution + for new immigrants, shape is num_per x S x J, where num_per + is the number of years between initial and final_data_year + income_percentiles (array_like): user provided income percentiles, + dimensions are J, the number of lifetime income groups; + defaults to a single income group (J=1) when no + income-specific inputs are supplied country_id (str): country id for UN data initial_data_year (int): initial year of data to use (not relevant if have user provided data) @@ -1000,16 +1441,14 @@ def get_pop_objs( - omega_path_lev[:-1, -S:].sum(axis=1) ) / omega_path_lev[:-1, -S:].sum(axis=1) g_n_path[fixper + 1 :] = g_n_SS - imm_rates_mat = np.concatenate( + imm_rates_full = np.concatenate( ( - imm_rates_orig[:fixper, E:], - np.tile( - imm_rates_adj[E:].reshape(1, S), - (T + S - fixper, 1), - ), + imm_rates_orig[:fixper, :], + np.tile(imm_rates_adj.reshape(1, E + S), (T + S - fixper, 1)), ), axis=0, ) + imm_rates_mat = imm_rates_full[:, E:] if GraphDiag: # Check whether original SS population distribution is close to @@ -1156,17 +1595,38 @@ def get_pop_objs( path=OUTPUT_DIR, ) + pop_objs = expand_pop_obj_J( + omega_path_lev, + omega_path_S, + omega_SSfx, + fert_rates, + mort_rates, + infmort_rates, + imm_rates_full, + mort_rates_S, + imm_rates_mat, + E, + S, + g_n_SS, + fixper, + income_percentiles=income_percentiles, + fert_gradient=fert_gradient, + mort_gradient=mort_gradient, + infmort_gradient=infmort_gradient, + imm_pctiles=imm_pctiles, + ) + # Return objects in a dictionary pop_dict = { - "omega": omega_path_S[1:, :], + "omega": pop_objs["omega_path_S"][1:, :, :], "g_n_ss": g_n_SS, - "omega_SS": omega_SSfx[-S:] / omega_SSfx[-S:].sum(), - "rho": mort_rates_S[1:, :], + "omega_SS": pop_objs["omega_SS"], + "rho": pop_objs["mort_rates_S"][1:, :, :], "g_n": g_n_path[1:], - "imm_rates": imm_rates_mat[1:, :], - "omega_S_preTP": omega_path_S[0, :], - "imm_rates_preTP": imm_rates_mat[0, :], - "rho_preTP": mort_rates_S[0, :], + "imm_rates": pop_objs["imm_rates_mat"][1:, :, :], + "omega_S_preTP": pop_objs["omega_path_S"][0, :, :], + "imm_rates_preTP": pop_objs["imm_rates_mat"][0, :, :], + "rho_preTP": pop_objs["mort_rates_S"][0, :, :], "g_n_preTP": g_n_path[0], } diff --git a/ogcore/fiscal.py b/ogcore/fiscal.py index 675a89961..e0378641d 100644 --- a/ogcore/fiscal.py +++ b/ogcore/fiscal.py @@ -24,7 +24,8 @@ def D_G_path(r, dg_fixed_values, p): .. math:: \begin{split} &e^{g_y}\left(1 + \tilde{g}_{n,t+1}\right)\hat{D}_{t+1} - + \hat{Rev}_t = (1 + r_{gov,t})\hat{D}_t + \hat{G}_t + + \hat{Rev}_t = (1 + r_{gov,t})\hat{D}_t + + p_{g,t}\hat{G}_t + p_{I_g,t}\hat{I}_{g,t} + \hat{TR}_t + \hat{UBI}_t \quad\forall t \\ &\hat{G}_t = g_{g,t}\:\alpha_{g}\: \hat{Y}_t \\ &\text{where}\quad g_{g,t} = @@ -50,12 +51,10 @@ def D_G_path(r, dg_fixed_values, p): Args: r_gov (Numpy array): interest rate on government debt over the time path - dg_fixed_values (tuple): (Y, total_tax_revenue, - agg_pension_outlays, UBI_outlays,TR, D0, G0) values of variables - that are taken as given in the government budget constraint - Gbaseline (Numpy array): government spending over the time path - in the baseline equilibrium, used only if - baseline_spending=True + dg_fixed_values (tuple): ``(Y, total_tax_revenue, + agg_pension_outlays, UBI_outlays, TR, I_g, p_g, p_Ig, + Gbaseline, D0_baseline)`` values taken as given in the + government budget constraint p (OG-Core Specifications object): model parameters Returns: @@ -77,6 +76,8 @@ def D_G_path(r, dg_fixed_values, p): UBI_outlays, TR, I_g, + p_g, + p_Ig, Gbaseline, D0_baseline, ) = dg_fixed_values @@ -113,8 +114,8 @@ def D_G_path(r, dg_fixed_values, p): while t < p.T - 1: D[t] = (1 / growth[t]) * ( (1 + r_gov[t - 1]) * D[t - 1] - + G[t - 1] - + I_g[t - 1] + + p_g[t - 1] * G[t - 1] + + p_Ig[t - 1] * I_g[t - 1] + TR[t - 1] + UBI_outlays[t - 1] + agg_pension_outlays[t - 1] @@ -130,10 +131,10 @@ def D_G_path(r, dg_fixed_values, p): + total_tax_revenue[t] + foreign_aid[t] - agg_pension_outlays[t] - - I_g[t - 1] + - p_Ig[t - 1] * I_g[t - 1] - TR[t] - UBI_outlays[t] - ) + ) / p_g[t] elif t >= p.tG2: G[t] = ( growth[t + 1] * (p.debt_ratio_ss * Y[t]) @@ -141,10 +142,10 @@ def D_G_path(r, dg_fixed_values, p): + total_tax_revenue[t] + foreign_aid[t] - agg_pension_outlays[t] - - I_g[t - 1] + - p_Ig[t - 1] * I_g[t - 1] - TR[t] - UBI_outlays[t] - ) + ) / p_g[t] t += 1 # in final period, growth rate has stabilized, so we can replace @@ -152,8 +153,8 @@ def D_G_path(r, dg_fixed_values, p): t = p.T - 1 D[t] = (1 / growth[t]) * ( (1 + r_gov[t - 1]) * D[t - 1] - + G[t - 1] - + I_g[t - 1] + + p_g[t - 1] * G[t - 1] + + p_Ig[t - 1] * I_g[t - 1] + TR[t - 1] + UBI_outlays[t - 1] + agg_pension_outlays[t - 1] @@ -167,14 +168,14 @@ def D_G_path(r, dg_fixed_values, p): + total_tax_revenue[t] + foreign_aid[t] - agg_pension_outlays[t] - - I_g[t - 1] + - p_Ig[t - 1] * I_g[t - 1] - TR[t] - UBI_outlays[t] - ) + ) / p_g[t] D[t + 1] = (1 / growth[t + 1]) * ( (1 + r_gov[t]) * D[t] - + G[t] - + I_g[t - 1] + + p_g[t] * G[t] + + p_Ig[t - 1] * I_g[t - 1] + TR[t] + UBI_outlays[t] + agg_pension_outlays[t] @@ -265,6 +266,8 @@ def get_G_ss( TR, UBI_outlays, I_g, + p_g, + p_Ig, new_borrowing, debt_service, p, @@ -273,8 +276,9 @@ def get_G_ss( Calculate the steady-state values of government spending. .. math:: - \bar{G} = \bar{Rev} + \bar{D}\bigl[(1 + \bar{g}_n)e^{g_y} - - (1 + \bar{r}_{gov})\bigr] - \bar{I}_g - \bar{TR} - \overline{UBI} + p_g\bar{G} = \bar{Rev} + \bar{D}\bigl[(1 + \bar{g}_n)e^{g_y} - + (1 + \bar{r}_{gov})\bigr] - p_{I_g}\bar{I}_g - \bar{TR} + - \overline{UBI} Args: Y (scalar): aggregate output @@ -283,6 +287,8 @@ def get_G_ss( TR (scalar): steady-state transfer spending UBI_outlays (scalar): steady-state total UBI outlays I_g (scalar): steady-state public infrastructure investment + p_g (scalar): price of the government consumption composite + p_Ig (scalar): price of the infrastructure investment composite new_borrowing (scalar): steady-state amount of new borrowing debt_service (scalar): steady-state debt service costs p (OG-Core Specifications object): model parameters @@ -299,8 +305,14 @@ def get_G_ss( total_tax_revenue + new_borrowing + foreign_aid - - (agg_pension_outlays + TR + debt_service + UBI_outlays + I_g) - ) + - ( + agg_pension_outlays + + TR + + debt_service + + UBI_outlays + + p_Ig * I_g + ) + ) / p_g return G @@ -331,6 +343,8 @@ def get_TR( agg_pension_outlays, UBI_outlays, I_g, + p_g, + p_Ig, p, method, ): @@ -356,6 +370,8 @@ def get_TR( outlays UBI_outlays (array_like): total universal basic income (UBI) outlays I_g (array_like): public infrastructure investment + p_g (array_like): price of the government consumption composite + p_Ig (array_like): price of the infrastructure investment composite p (OG-Core Specifications object): model parameters method (str): whether doing SS or TP calculation @@ -372,9 +388,9 @@ def get_TR( total_tax_revenue + foreign_aid - agg_pension_outlays - - G + - p_g * G - UBI_outlays - - I_g + - p_Ig * I_g ) elif p.baseline_spending: new_TR = p.alpha_bs_T[-1] * TR diff --git a/ogcore/household.py b/ogcore/household.py index c567867da..4b5c068d5 100644 --- a/ogcore/household.py +++ b/ogcore/household.py @@ -176,26 +176,21 @@ def get_bq(BQ, j, p, method): if p.use_zeta: if j is not None: if method == "SS": - bq = (p.zeta[:, j] * BQ) / (p.lambdas[j] * p.omega_SS) + bq = (p.zeta[:, j] * BQ) / p.omega_SS[:, j] else: len_T = BQ.shape[0] bq = ( np.reshape(p.zeta[:, j], (1, p.S)) * BQ.reshape((len_T, 1)) - ) / (p.lambdas[j] * p.omega[:len_T, :]) + ) / p.omega[:len_T, :, j] else: if method == "SS": - bq = (p.zeta * BQ) / ( - p.lambdas.reshape((1, p.J)) * p.omega_SS.reshape((p.S, 1)) - ) + bq = (p.zeta * BQ) / p.omega_SS else: len_T = BQ.shape[0] bq = ( np.reshape(p.zeta, (1, p.S, p.J)) * utils.to_timepath_shape(BQ) - ) / ( - p.lambdas.reshape((1, 1, p.J)) - * p.omega[:len_T, :].reshape((len_T, p.S, 1)) - ) + ) / p.omega[:len_T, :, :] else: if j is not None: if method == "SS": @@ -237,22 +232,19 @@ def get_tr(TR, j, p, method): """ if j is not None: if method == "SS": - tr = (p.eta[-1, :, j] * TR) / (p.lambdas[j] * p.omega_SS) + tr = (p.eta[-1, :, j] * TR) / p.omega_SS[:, j] else: len_T = TR.shape[0] tr = (p.eta[:len_T, :, j] * TR.reshape((len_T, 1))) / ( - p.lambdas[j] * p.omega[:len_T, :] + p.omega[:len_T, :, j] ) else: if method == "SS": - tr = (p.eta[-1, :, :] * TR) / ( - p.lambdas.reshape((1, p.J)) * p.omega_SS.reshape((p.S, 1)) - ) + tr = (p.eta[-1, :, :] * TR) / p.omega_SS else: len_T = TR.shape[0] tr = (p.eta[:len_T, :, :] * utils.to_timepath_shape(TR)) / ( - p.lambdas.reshape((1, 1, p.J)) - * p.omega[:len_T, :].reshape((len_T, p.S, 1)) + p.omega[:len_T, :, :] ) return tr @@ -279,22 +271,19 @@ def get_rm(RM, j, p, method): """ if j is not None: if method == "SS": - rm = (p.eta_RM[-1, :, j] * RM) / (p.lambdas[j] * p.omega_SS) + rm = (p.eta_RM[-1, :, j] * RM) / p.omega_SS[:, j] else: len_T = RM.shape[0] rm = (p.eta_RM[:len_T, :, j] * RM.reshape((len_T, 1))) / ( - p.lambdas[j] * p.omega[:len_T, :] + p.omega[:len_T, :, j] ) else: if method == "SS": - rm = (p.eta_RM[-1, :, :] * RM) / ( - p.lambdas.reshape((1, p.J)) * p.omega_SS.reshape((p.S, 1)) - ) + rm = (p.eta_RM[-1, :, :] * RM) / p.omega_SS else: len_T = RM.shape[0] rm = (p.eta_RM[:len_T, :, :] * utils.to_timepath_shape(RM)) / ( - p.lambdas.reshape((1, 1, p.J)) - * p.omega[:len_T, :].reshape((len_T, p.S, 1)) + p.omega[:len_T, :, :] ) return rm @@ -541,7 +530,7 @@ def FOC_savings( h_wealth = p.h_wealth[-1] m_wealth = p.m_wealth[-1] p_wealth = p.p_wealth[-1] - p_tilde = np.ones_like(p.rho[-1, :]) * p_tilde + p_tilde = np.ones_like(rho) * p_tilde tau_c = p.tau_c[-1, :] elif method == "TPI_scalar": h_wealth = p.h_wealth[0] diff --git a/ogcore/model_variables.json b/ogcore/model_variables.json index 6c26cee6a..ca4fbbbf2 100644 --- a/ogcore/model_variables.json +++ b/ogcore/model_variables.json @@ -386,6 +386,24 @@ "label": "Consumption good prices ($p_{i,t}$)", "toGDP_label": "" }, + "p_g": { + "desc": "Price of the government consumption composite good", + "section": "Prices", + "type": "array-like", + "TPI dimensions": "T", + "SS dimensions": "scalar", + "label": "Government consumption good price ($p_{g,t}$)", + "toGDP_label": "" + }, + "p_Ig": { + "desc": "Price of the infrastructure investment composite good", + "section": "Prices", + "type": "array-like", + "TPI dimensions": "T", + "SS dimensions": "scalar", + "label": "Infrastructure investment good price ($p_{I_g,t}$)", + "toGDP_label": "" + }, "p_tilde": { "desc": "Price of composite output good", "section": "Prices", @@ -566,4 +584,4 @@ "label": "Resource Constraint Error", "toGDP_label": "" } -} \ No newline at end of file +} diff --git a/ogcore/output_plots.py b/ogcore/output_plots.py index ae84716ca..adea5a747 100644 --- a/ogcore/output_plots.py +++ b/ogcore/output_plots.py @@ -595,9 +595,12 @@ def ability_bar( ind = np.arange(N) # the x locations for the groups width = 0.2 # the width of the bars start_index = start_year - base_params.start_year - omega_to_use = base_params.omega[: base_params.T, :].reshape( - base_params.T, base_params.S, 1 - ) + if base_params.omega.ndim == 3: + omega_to_use = base_params.omega[: base_params.T, :, :] + else: + omega_to_use = base_params.omega[: base_params.T, :].reshape( + base_params.T, base_params.S, 1 + ) * base_params.lambdas.reshape(1, 1, base_params.J) base_val = ( (base_tpi[var] * omega_to_use)[ start_index : start_index + num_years, :, : @@ -659,12 +662,24 @@ def ability_bar_ss( fig, ax = plt.subplots() ind = np.arange(N) # the x locations for the groups width = 0.2 # the width of the bars - base_val = ( - base_ss[var] * base_params.omega_SS.reshape(base_params.S, 1) - ).sum(0) - reform_val = ( - reform_ss[var] * reform_params.omega_SS.reshape(reform_params.S, 1) - ).sum(0) + if base_params.omega_SS.ndim == 2: + base_val = (base_ss[var] * base_params.omega_SS).sum(0) + reform_val = (reform_ss[var] * reform_params.omega_SS).sum(0) + else: + base_val = ( + base_ss[var] + * ( + base_params.omega_SS.reshape(base_params.S, 1) + * base_params.lambdas.reshape(1, base_params.J) + ) + ).sum(0) + reform_val = ( + reform_ss[var] + * ( + reform_params.omega_SS.reshape(reform_params.S, 1) + * reform_params.lambdas.reshape(1, reform_params.J) + ) + ).sum(0) var_to_plot = (reform_val - base_val) / base_val ax.bar(ind, var_to_plot * 100, width, bottom=0) ax.set_xticks(ind + width / 4) diff --git a/ogcore/output_tables.py b/ogcore/output_tables.py index d719caa64..9f75ee69f 100644 --- a/ogcore/output_tables.py +++ b/ogcore/output_tables.py @@ -752,9 +752,7 @@ def dynamic_revenue_decomposition( None, "TPI", ).sum(axis=-1) - pop_weights = np.squeeze(base_params.lambdas) * np.tile( - np.reshape(base_params.omega[:T, :], (T, S, 1)), (1, 1, J) - ) + pop_weights = base_params.omega[:T, :, :] for k in indiv_liab.keys(): tax_rev_dict["indiv"][k] = (indiv_liab[k] * pop_weights).sum(1).sum(1) tax_rev_dict["total"][k] = ( diff --git a/ogcore/parameter_plots.py b/ogcore/parameter_plots.py index 6b79db1e8..ee9b2e0a3 100644 --- a/ogcore/parameter_plots.py +++ b/ogcore/parameter_plots.py @@ -89,14 +89,25 @@ def plot_mort_rates( for y in years: t = y - p0.start_year for i, p in enumerate(p_list): + # get average mortality rate across all j types if 3D array + if p.rho.ndim == 3: + # average over all j types + if p.omega.ndim == 3: + rho_t = (p.rho[t, :, :] * p.omega[t, :, :]).sum(axis=-1) + else: + rho_t = ( + p.rho[t, :, :] * p.lambdas.reshape(1, 1, p.J) + ).sum(axis=-1) + else: + rho_t = p.rho[t, :] if survival_rates: plt.plot( age_per, - np.cumprod(1 - p.rho[t, :]), + np.cumprod(1 - rho_t), label=labels[i] + " " + str(y), ) else: - plt.plot(age_per, p.rho[t, :], label=labels[i] + " " + str(y)) + plt.plot(age_per, rho_t, label=labels[i] + " " + str(y)) plt.xlabel(r"Age $s$ (model periods)") if survival_rates: plt.ylabel(r"Cumulative Survival Rates") diff --git a/ogcore/parameters.py b/ogcore/parameters.py index f88dd79cb..4ac862cd0 100644 --- a/ogcore/parameters.py +++ b/ogcore/parameters.py @@ -87,6 +87,16 @@ def compute_default_params(self): self.T = int(self.T) self.J = len(self.lambdas) + io_matrix = np.asarray(self.io_matrix) + expected_io_shape = (self.I + 2, self.M) + if io_matrix.shape != expected_io_shape: + raise ValueError( + "io_matrix must have shape " + f"(I + 2, M)={expected_io_shape}; got {io_matrix.shape}." + ) + if not np.allclose(io_matrix.sum(axis=1), 1.0): + raise ValueError("Each row of io_matrix must sum to 1.") + # beta_annual and chi_b carry one value per lifetime-income group, but # paramtools validates values and nesting depth, not list length, so a # wrong-length vector used to flow through silently: a short one @@ -241,15 +251,16 @@ def compute_default_params(self): ) setattr(self, item, param_out) # Deal with parameters that vary across age and over time - tp_param_list4 = [ - "rho", - ] - for item in tp_param_list4: - param_in = getattr(self, item) - param_out = extrapolate_array( - param_in, dims=(self.T + self.S, self.S), item=item - ) - setattr(self, item, param_out) + # None at the moment - moved to vary by J also + # tp_param_list4 = [ + # "rho", + # ] + # for item in tp_param_list4: + # param_in = getattr(self, item) + # param_out = extrapolate_array( + # param_in, dims=(self.T + self.S, self.S), item=item + # ) + # setattr(self, item, param_out) # Deal with tax parameters that maybe age and time specific tax_params_to_TP = [ "etr_params", @@ -276,16 +287,26 @@ def compute_default_params(self): # Try to deal with size of eta and eta_RM. They may vary by S, J, T, # but want to allow user to enter one that varies by only S, S and J, # S and T, or T and S and J. - eta_params_to_TP = [ - "eta", - "eta_RM", - ] + eta_params_to_TP = ["eta", "eta_RM", "omega", "rho", "imm_rates"] for item in eta_params_to_TP: param_in = getattr(self, item) param_out = extrapolate_array( param_in, dims=(self.T + self.S, self.S, self.J), item=item ) setattr(self, item, param_out) + # extrapolate SS demog objects + demog_obj_to_SS = [ + "omega_SS", + "omega_S_preTP", + "imm_rates_preTP", + "rho_preTP", + ] + for item in demog_obj_to_SS: + param_in = getattr(self, item) + param_out = extrapolate_array( + param_in, dims=(self.S, self.J), item=item + ) + setattr(self, item, param_out) # extrapolate lifetime ability e matrix over time dimension param_in = getattr(self, "e") param_out = extrapolate_array( @@ -344,19 +365,23 @@ def compute_default_params(self): self.g_n_ss = 0.0 self.g_n_preTP = 0.0 self.g_n = np.zeros(self.T + self.S) - surv_rate = np.ones_like(self.rho) - self.rho + # Average rho over J for scalar survival computation + rho_ss = self.rho[-1, :, :].mean(axis=-1) # (S,) surv_rate1 = np.ones((self.S,)) # prob start at age S - surv_rate1[1:] = np.cumprod(surv_rate[-1, :-1], dtype=float) + surv_rate1[1:] = np.cumprod(1 - rho_ss[:-1], dtype=float) # number of each age alive at any time - omega_SS = np.ones(self.S) * surv_rate1 - self.omega_SS = omega_SS / omega_SS.sum() - self.imm_rates = np.zeros((self.T + self.S, self.S)) + omega_SS_1d = surv_rate1 / surv_rate1.sum() # (S,) + # Expand to (S, J) joint distribution + lambdas_arr = np.array(self.lambdas).reshape(1, self.J) + self.omega_SS = omega_SS_1d.reshape(self.S, 1) * lambdas_arr + self.imm_rates = np.zeros((self.T + self.S, self.S, self.J)) self.omega = np.tile( - np.reshape(self.omega_SS, (1, self.S)), (self.T + self.S, 1) + self.omega_SS.reshape(1, self.S, self.J), + (self.T + self.S, 1, 1), ) - self.omega_S_preTP = self.omega_SS - self.imm_rates_preTP = np.zeros(self.S) - self.rho_preTP = self.rho[0, :] + self.omega_S_preTP = self.omega_SS.copy() + self.imm_rates_preTP = np.zeros((self.S, self.J)) + self.rho_preTP = self.rho[0, :, :] # Create time series of stationarized UBI transfers self.ubi_nom_array = self.get_ubi_nom_objs() diff --git a/ogcore/pensions.py b/ogcore/pensions.py index 0727ba8c2..af504f9b4 100644 --- a/ogcore/pensions.py +++ b/ogcore/pensions.py @@ -213,7 +213,10 @@ def DB_amount(w, e, n, j, p): Args: w (array_like): real wage rate e (Numpy array): effective labor units - n (Numpy array): labor supply + n (Numpy array): labor supply; may be a partial lifetime path + (length < S, for cohorts alive when the time path begins), + a steady-state vector (S,) or matrix (S, J), or the full + time path (T, S, J) j (int): index of lifetime income group p (OG-Core Specifications object): model parameters @@ -226,23 +229,38 @@ def DB_amount(w, e, n, j, p): equiv_periods = int(round((p.S / 80.0) * p.avg_earn_num_years)) - 1 equiv_yr_contrib = int(round((p.S / 80.0) * p.yr_contrib)) - 1 L_inc_avg_s = np.zeros(equiv_periods) + # Retirement age is potentially time varying (PR #433), but the DB + # system supports a single retirement age for now; use the + # steady-state value (see Issue #1014). g_y is a scalar parameter, + # but the numba loops index it, so pass it as a 1-element array. + S_ret = int(np.asarray(p.retire).flat[-1]) + g_y_arr = np.atleast_1d(np.asarray(p.g_y, dtype=float)).ravel() + # The wage may arrive as a scalar (steady state, or one remaining + # period in the TPI); the loops index it as a path, so broadcast it + # to the length of the labor-supply vector. + if np.ndim(w) == 0: + w = np.full(n.shape[0], float(w)) if n.shape[0] < p.S: per_rmn = n.shape[0] - # TODO: think about how to handle setting w_preTP and n_preTP - # TODO: will need to update how the e matrix is handled here - # and else where to allow for it to be time varying - w_S = np.append((p.w_preTP * np.ones(p.S))[:(-per_rmn)], w) + # Pre-time-path wages: the recent past is anchored to the + # period-0 wage of the current path (trend growth is handled + # by the de-trending inside the benefit formula); labor comes + # from the model's initial condition n_preTP (see Issue #1014) + w_S = np.append((w[0] * np.ones(p.S))[:(-per_rmn)], w) n_S = np.append(p.n_preTP[:(-per_rmn), j], n) DB = np.zeros(p.S) DB = DB_1dim_loop( w_S, - p.e[:, j], + # TODO: will need to update how the e matrix is handled + # here and elsewhere to allow for it to be time varying + # (see Issue #1014); use the steady-state profile for now + p.e[-1, :, j] if np.ndim(p.e) == 3 else p.e[:, j], n_S, - p.retire, + S_ret, p.S, - p.g_y, + g_y_arr, L_inc_avg_s, L_inc_avg, DB, @@ -259,9 +277,9 @@ def DB_amount(w, e, n, j, p): w, e, n, - p.retire, + S_ret, p.S, - p.g_y, + g_y_arr, L_inc_avg_s, L_inc_avg, DB, @@ -277,9 +295,9 @@ def DB_amount(w, e, n, j, p): w, e, n, - p.retire, + S_ret, p.S, - p.g_y, + g_y_arr, L_inc_avg_sj, L_inc_avg, DB, @@ -288,6 +306,26 @@ def DB_amount(w, e, n, j, p): equiv_yr_contrib, ) + elif np.ndim(n) == 3: + T = n.shape[0] + w_path = np.squeeze(np.asarray(w)) + if w_path.ndim == 0: + w_path = np.full(T, float(w_path)) + e_ss = p.e[-1] if np.ndim(p.e) == 3 else p.e + DB = DB_3dim_loop( + w_path, + e_ss, + n, + p.n_preTP, + S_ret, + p.S, + p.J, + float(g_y_arr[-1]), + equiv_periods, + p.alpha_db, + equiv_yr_contrib, + ) + return DB @@ -315,22 +353,34 @@ def NDC_amount(w, e, n, r, Y, j, p): """ g_ndc_amount = g_ndc(r, Y, p) delta_ret_amount = delta_ret(r, Y, p) + # Single retirement age for now (see Issue #1014); g_y is a scalar + # but the numba loops index it; the steady-state wage is a scalar + # but the loops index it as a path. + S_ret = int(np.asarray(p.retire).flat[-1]) + g_y_arr = np.atleast_1d(np.asarray(p.g_y, dtype=float)).ravel() + if np.ndim(w) == 0: + w = np.full(n.shape[0], float(w)) if n.shape[0] < p.S: per_rmn = n.shape[0] - w_S = np.append((p.w_preTP * np.ones(p.S))[:(-per_rmn)], w) + # Pre-time-path wages: the recent past is anchored to the + # period-0 wage of the current path (trend growth is handled + # by the de-trending inside the benefit formula); labor comes + # from the model's initial condition n_preTP (see Issue #1014) + w_S = np.append((w[0] * np.ones(p.S))[:(-per_rmn)], w) n_S = np.append(p.n_preTP[:(-per_rmn), j], n) - NDC_s = np.zeros(p.retire) + NDC_s = np.zeros(S_ret) NDC = np.zeros(p.S) NDC = NDC_1dim_loop( w_S, - p.e[:, j], + # steady-state earnings profile for now (see Issue #1014) + p.e[-1, :, j] if np.ndim(p.e) == 3 else p.e[:, j], n_S, - p.retire, + S_ret, p.S, - p.g_y, + g_y_arr, p.tau_p, g_ndc_amount, delta_ret_amount, @@ -341,15 +391,15 @@ def NDC_amount(w, e, n, r, Y, j, p): else: if np.ndim(n) == 1: - NDC_s = np.zeros(p.retire) + NDC_s = np.zeros(S_ret) NDC = np.zeros(p.S) NDC = NDC_1dim_loop( w, e, n, - p.retire, + S_ret, p.S, - p.g_y, + g_y_arr, p.tau_p, g_ndc_amount, delta_ret_amount, @@ -357,15 +407,15 @@ def NDC_amount(w, e, n, r, Y, j, p): NDC, ) elif np.ndim(n) == 2: - NDC_sj = np.zeros((p.retire, p.J)) + NDC_sj = np.zeros((S_ret, p.J)) NDC = np.zeros((p.S, p.J)) NDC = NDC_2dim_loop( w, e, n, - p.retire, + S_ret, p.S, - p.g_y, + g_y_arr, p.tau_p, g_ndc_amount, delta_ret_amount, @@ -397,19 +447,32 @@ def PS_amount(w, e, n, j, factor, p): PS (Numpy array): pension amount for each household """ + # Single retirement age for now (see Issue #1014); g_y is + # a scalar but the numba loops index it; the steady-state wage is a + # scalar but the loops index it as a path. + S_ret = int(np.asarray(p.retire).flat[-1]) + g_y_arr = np.atleast_1d(np.asarray(p.g_y, dtype=float)).ravel() + if np.ndim(w) == 0: + w = np.full(n.shape[0], float(w)) + if n.shape[0] < p.S: per_rmn = n.shape[0] - w_S = np.append((p.w_preTP * np.ones(p.S))[:(-per_rmn)], w) + # Pre-time-path wages: the recent past is anchored to the + # period-0 wage of the current path (trend growth is handled + # by the de-trending inside the benefit formula); labor comes + # from the model's initial condition n_preTP (see Issue #1014) + w_S = np.append((w[0] * np.ones(p.S))[:(-per_rmn)], w) n_S = np.append(p.n_preTP[:(-per_rmn), j], n) - L_inc_avg_s = np.zeros(p.retire) + L_inc_avg_s = np.zeros(S_ret) PS = np.zeros(p.S) PS = PS_1dim_loop( w_S, - p.e[:, j], + # steady-state earnings profile for now (see Issue #1014) + p.e[-1, :, j] if np.ndim(p.e) == 3 else p.e[:, j], n_S, - p.retire, + S_ret, p.S, - p.g_y, + g_y_arr, p.vpoint, factor, L_inc_avg_s, @@ -419,15 +482,15 @@ def PS_amount(w, e, n, j, factor, p): else: if np.ndim(n) == 1: - L_inc_avg_s = np.zeros(p.retire) + L_inc_avg_s = np.zeros(S_ret) PS = np.zeros(p.S) PS = PS_1dim_loop( w, e, n, - p.retire, + S_ret, p.S, - p.g_y, + g_y_arr, p.vpoint, factor, L_inc_avg_s, @@ -435,16 +498,16 @@ def PS_amount(w, e, n, j, factor, p): ) elif np.ndim(n) == 2: - L_inc_avg_sj = np.zeros((p.retire, p.J)) + L_inc_avg_sj = np.zeros((S_ret, p.J)) PS = np.zeros((p.S, p.J)) PS = PS_2dim_loop( w, e, n, - p.retire, + S_ret, p.S, p.J, - p.g_y, + g_y_arr, p.vpoint, factor, L_inc_avg_sj, @@ -513,9 +576,11 @@ def deriv_NDC(r, w, e, Y, per_rmn, p): d_theta (Numpy array): change in NDC pension benefits for another unit of labor supply """ + # Single retirement age for now (see Issue #1014) + S_ret = int(np.asarray(p.retire).flat[-1]) if per_rmn == 1: d_theta = 0 - elif per_rmn < (p.S - p.retire + 1): + elif per_rmn < (p.S - S_ret + 1): d_theta = np.zeros(per_rmn) else: d_theta_empty = np.zeros(per_rmn) @@ -526,7 +591,7 @@ def deriv_NDC(r, w, e, Y, per_rmn, p): e, per_rmn, p.S, - p.retire, + S_ret, p.tau_p, g_ndc_amount, delta_ret_amount, @@ -561,14 +626,16 @@ def deriv_DB(w, e, per_rmn, p): """ equiv_periods = int(round((p.S / 80.0) * p.avg_earn_num_years)) - 1 equiv_yr_contrib = int(round((p.S / 80.0) * p.yr_contrib)) - 1 - if per_rmn < (p.S - p.retire + 1): + # Single retirement age for now (see Issue #1014) + S_ret = int(np.asarray(p.retire).flat[-1]) + if per_rmn < (p.S - S_ret + 1): d_theta = np.zeros(p.S) else: d_theta = deriv_DB_loop( w, e, p.S, - p.retire, + S_ret, per_rmn, equiv_periods, p.alpha_db, @@ -602,12 +669,14 @@ def deriv_PS(w, e, per_rmn, factor, p): """ - if per_rmn < (p.S - p.retire + 1): + # Single retirement age for now (see Issue #1014) + S_ret = int(np.asarray(p.retire).flat[-1]) + if per_rmn < (p.S - S_ret + 1): d_theta = np.zeros(p.S) else: d_theta_empty = np.zeros(p.S) d_theta = deriv_PS_loop( - w, e, p.S, p.retire, per_rmn, d_theta_empty, p.vpoint, factor + w, e, p.S, S_ret, per_rmn, d_theta_empty, p.vpoint, factor ) d_theta = d_theta[-per_rmn:] @@ -661,14 +730,14 @@ def g_ndc(r, Y, p): g_ndc (Numpy array): growth rate used for contributions to NDC """ + # r and g_y are scalars in the SS but paths in the TPI; take the + # last element robustly in either case (see Issue #1169) if p.ndc_growth_rate == "r": - g_ndc = r[-1] + g_ndc = np.asarray(r).flat[-1] elif p.ndc_growth_rate == "Curr GDP": g_ndc = (Y[1:] - Y[:-1]) / Y[:-1] - elif p.ndc_growth_rate == "LR GDP": - g_ndc = p.g_y[-1] + p.g_n[-1] - else: - g_ndc = p.g_y[-1] + p.g_n[-1] + else: # "LR GDP" + g_ndc = np.asarray(p.g_y).flat[-1] + np.asarray(p.g_n).flat[-1] return g_ndc @@ -688,14 +757,14 @@ def g_dir(r, Y, g_y, g_n, dir_growth_rate): g_dir (Numpy array): growth rate used for contributions to NDC """ + # r and g_y are scalars in the SS but paths in the TPI; take the + # last element robustly in either case (see Issue #1169) if dir_growth_rate == "r": - g_dir = r[-1] + g_dir = np.asarray(r).flat[-1] elif dir_growth_rate == "Curr GDP": g_dir = (Y[1:] - Y[:-1]) / Y[:-1] - elif dir_growth_rate == "LR GDP": - g_dir = g_y[-1] + g_n[-1] - else: - g_dir = g_y[-1] + g_n[-1] + else: # "LR GDP" + g_dir = np.asarray(g_y).flat[-1] + np.asarray(g_n).flat[-1] return g_dir @@ -717,11 +786,20 @@ def delta_ret(r, Y, p): pension amount """ - surv_rates = 1 - p.mort_rates_SS - dir_delta_s_empty = np.zeros(p.S - p.retire + 1) + # A real Specifications object has no mort_rates_SS attribute; fall + # back to the steady-state mortality rates implied by rho, averaged + # over lifetime-income groups with their population weights (see + # Issue #1169) + mort_rates_SS = getattr(p, "mort_rates_SS", None) + if mort_rates_SS is None: + mort_rates_SS = p.rho[-1] @ np.asarray(p.lambdas).ravel() + surv_rates = 1 - mort_rates_SS + # Single retirement age for now (see Issue #1014) + S_ret = int(np.asarray(p.retire).flat[-1]) + dir_delta_s_empty = np.zeros(p.S - S_ret + 1) g_dir_value = g_dir(r, Y, p.g_y, p.g_n, p.dir_growth_rate) dir_delta = delta_ret_loop( - p.S, p.retire, surv_rates, g_dir_value, dir_delta_s_empty + p.S, S_ret, surv_rates, g_dir_value, dir_delta_s_empty ) delta_ret = 1 / (dir_delta + p.indR - p.k_ret) @@ -1027,6 +1105,73 @@ def DB_2dim_loop( @numba.jit(nopython=True) +def DB_3dim_loop( + w_path, + e_ss, + n, + n_preTP, + S_ret, + S, + J, + g_y, + avg_earn_num_years, + alpha_db, + yr_contr, +): + r""" + Calculate public pension from a defined benefits system over the + full time path. + + Used when the TPI solution evaluates taxes for all periods at once + to compute aggregates. Each retiree's benefit is computed from their + own cohort's wage and labor history: the wage entering the average + for a household aged u at time t, earned at age s, is the wage at + time t - (u - s). Histories that predate the time path use the + period-0 wage (trend growth is handled by the de-trending in the + benefit formula) and the model's initial labor supply, so this + reproduces exactly what the per-cohort household solves compute and + household behavior and aggregates are consistent. The inner work is + vectorized rather than looped. + + Args: + w_path (Numpy array): real wage rate path, length T + e_ss (Numpy array): effective labor units, size SxJ + n (Numpy array): labor supply, size TxSxJ + n_preTP (Numpy array): pre-time-path labor supply, size SxJ + S_ret (int): retirement age + S (int): number of periods in the model + J (int): number of lifetime income groups + g_y (scalar): growth rate of technology + avg_earn_num_years (int): number of years earnings are averaged + over + alpha_db (scalar): replacement rate per year of contribution + yr_contr (int): years of contribution + + Returns: + DB (Numpy array): pension amount for each household, size TxSxJ + """ + T = n.shape[0] + s_idx = np.arange(S_ret - avg_earn_num_years, S_ret) + DB = np.zeros((T, S, J)) + for t in range(T): + for u in range(S_ret, S): + tau = t - (u - s_idx) + w_hist = w_path[np.clip(tau, 0, T - 1)] + n_hist = np.where( + (tau >= 0)[:, None], + n[np.clip(tau, 0, T - 1), s_idx, :], + n_preTP[s_idx, :], + ) + L = (w_hist[:, None] / np.exp(g_y * (u - s_idx))[:, None]) * ( + e_ss[s_idx, :] * n_hist + ) + DB[t, u, :] = ( + (L.sum(axis=0) / avg_earn_num_years) * yr_contr * alpha_db + ) + + return DB + + def NDC_1dim_loop(w, e, n, S_ret, S, g_y, tau_p, g_ndc, delta_ret, NDC_s, NDC): """ Calculate public pension from a notional defined contribution diff --git a/ogcore/tax.py b/ogcore/tax.py index 4a177dc6d..87afc26d0 100644 --- a/ogcore/tax.py +++ b/ogcore/tax.py @@ -338,16 +338,39 @@ def income_tax_liab(r, w, b, n, factor, t, j, method, e, etr_params, p): """ if j is not None: if method == "TPI": + length = r.shape[0] if b.ndim == 2: r = r.reshape(r.shape[0], 1) w = w.reshape(w.shape[0], 1) + labor_income_tax_compliance_rate = ( + p.labor_income_tax_noncompliance_rate[ + t : t + length, j + ].reshape(length, 1) + ) + capital_income_tax_compliance_rate = ( + p.capital_income_tax_noncompliance_rate[ + t : t + length, j + ].reshape(length, 1) + ) + tax_filer = p.income_tax_filer[t : t + length, j].reshape( + length, 1 + ) + else: + labor_income_tax_compliance_rate = ( + p.labor_income_tax_noncompliance_rate[t : t + length, j] + ) + capital_income_tax_compliance_rate = ( + p.capital_income_tax_noncompliance_rate[t : t + length, j] + ) + tax_filer = p.income_tax_filer[t : t + length, j] + elif method == "TPI_scalar": labor_income_tax_compliance_rate = ( - p.labor_income_tax_noncompliance_rate[t, j] + p.labor_income_tax_noncompliance_rate[0, j] ) capital_income_tax_compliance_rate = ( - p.capital_income_tax_noncompliance_rate[t, j] + p.capital_income_tax_noncompliance_rate[0, j] ) - tax_filer = p.income_tax_filer[t, j] + tax_filer = p.income_tax_filer[0, j] else: labor_income_tax_compliance_rate = ( p.labor_income_tax_noncompliance_rate[-1, j] @@ -360,13 +383,20 @@ def income_tax_liab(r, w, b, n, factor, t, j, method, e, etr_params, p): if method == "TPI": r = utils.to_timepath_shape(r) w = utils.to_timepath_shape(w) + length = r.shape[0] labor_income_tax_compliance_rate = ( - p.labor_income_tax_noncompliance_rate[t, :] + p.labor_income_tax_noncompliance_rate[ + t : t + length, : + ].reshape(length, 1, p.J) ) capital_income_tax_compliance_rate = ( - p.capital_income_tax_noncompliance_rate[t, :] + p.capital_income_tax_noncompliance_rate[ + t : t + length, : + ].reshape(length, 1, p.J) + ) + tax_filer = p.income_tax_filer[t : t + length, :].reshape( + length, 1, p.J ) - tax_filer = p.income_tax_filer[t, :] else: labor_income_tax_compliance_rate = ( p.labor_income_tax_noncompliance_rate[-1, :] diff --git a/ogcore/utils.py b/ogcore/utils.py index 4fdbba72a..073dc4dc6 100644 --- a/ogcore/utils.py +++ b/ogcore/utils.py @@ -469,9 +469,13 @@ def __init__(self, dist, pop_weights, ability_weights, S, J): self.dist = dist self.pop_weights = pop_weights self.ability_weights = ability_weights - weights = np.tile( - pop_weights.reshape(S, 1), (1, J) - ) * ability_weights.reshape(1, J) + if np.asarray(pop_weights).ndim == 2: + # pop_weights is already (S, J) joint distribution + weights = np.asarray(pop_weights) + else: + weights = np.tile( + pop_weights.reshape(S, 1), (1, J) + ) * ability_weights.reshape(1, J) flattened_dist = dist.flatten() flattened_weights = weights.flatten() idx = np.argsort(flattened_dist) @@ -494,7 +498,11 @@ def gini(self, type="overall"): nu = np.cumsum(self.sort_dist * self.sort_weights) elif type == "age": flattened_dist = self.dist.sum(axis=1).flatten() - flattened_weights = self.pop_weights.flatten() + pw = np.asarray(self.pop_weights) + if pw.ndim == 2: + flattened_weights = pw.sum(axis=1).flatten() + else: + flattened_weights = pw.flatten() idx = np.argsort(flattened_dist) sort_dist = flattened_dist[idx] sort_weights = flattened_weights[idx] / flattened_weights.sum() @@ -1006,15 +1014,15 @@ def extrapolate_array(param_in, dims=None, item="Parameter Name"): ), axis=0, ) + # case if T+S by S input elif param_in.shape[0] == dims[0]: - param_in = ( + param_out = ( np.tile( param_in.reshape(dims[0], dims[1], 1), (1, 1, dims[2]), ) / dims[2] ) - param_out = param_in else: print(item + " dimensions are: ", param_in.shape) print("please give an " + item + " that is either SxJ or TxS") diff --git a/pyproject.toml b/pyproject.toml index 7e3a57e1f..a1e6d062e 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -4,7 +4,7 @@ build-backend = "setuptools.build_meta" [project] name = "ogcore" -version = "0.16.4" +version = "0.19.0" authors = [ {name = "Jason DeBacker and Richard W. Evans"}, ] diff --git a/scripts/QUICK_INSTALL.md b/scripts/QUICK_INSTALL.md index 8707e7185..6a54ed8e9 100644 --- a/scripts/QUICK_INSTALL.md +++ b/scripts/QUICK_INSTALL.md @@ -50,6 +50,7 @@ By default the installer shows a menu of repos and prompts for a destination. Fl - `og-zaf` — South Africa ([EAPD-DRB/OG-ZAF](https://github.com/EAPD-DRB/OG-ZAF)) - `og-idn` — Indonesia ([EAPD-DRB/OG-IDN](https://github.com/EAPD-DRB/OG-IDN)) - `og-phl` — Philippines ([EAPD-DRB/OG-PHL](https://github.com/EAPD-DRB/OG-PHL)) + - `og-bra` — Brazil ([PSLmodels/OG-BRA](https://github.com/PSLmodels/OG-BRA)) - `--all` / `-All` — install every repo in the catalog (each into its own subfolder). - `--list` / `-List` and `--list-json` / `-ListJson` — print the repo catalog (human-readable or JSON) and exit, without installing anything. The same catalog is also published as a static file you can fetch directly: [`scripts/repos.json`](repos.json). - `--repo-url` / `-RepoUrl` — a full git URL for a single custom repo (a fork, or a country repo not yet in the catalog). Clones the default branch. diff --git a/scripts/install.ps1 b/scripts/install.ps1 index b49a53d8d..088a70a96 100644 --- a/scripts/install.ps1 +++ b/scripts/install.ps1 @@ -91,7 +91,8 @@ $Repos = @( [pscustomobject]@{ Key="og-eth"; Owner="EAPD-DRB"; Name="OG-ETH"; Pkg="ogeth"; Desc="Ethiopia" }, [pscustomobject]@{ Key="og-zaf"; Owner="EAPD-DRB"; Name="OG-ZAF"; Pkg="ogzaf"; Desc="South Africa" }, [pscustomobject]@{ Key="og-idn"; Owner="EAPD-DRB"; Name="OG-IDN"; Pkg="ogidn"; Desc="Indonesia" }, - [pscustomobject]@{ Key="og-phl"; Owner="EAPD-DRB"; Name="OG-PHL"; Pkg="ogphl"; Desc="Philippines" } + [pscustomobject]@{ Key="og-phl"; Owner="EAPD-DRB"; Name="OG-PHL"; Pkg="ogphl"; Desc="Philippines" }, + [pscustomobject]@{ Key="og-bra"; Owner="PSLmodels"; Name="OG-BRA"; Pkg="ogbra"; Desc="Brazil" } ) # -- Catalog listing (-List / -ListJson) --------------------------------------- diff --git a/scripts/install.sh b/scripts/install.sh index 8fbeaee62..f1e714303 100644 --- a/scripts/install.sh +++ b/scripts/install.sh @@ -31,6 +31,7 @@ REPOS=( "og-zaf|EAPD-DRB|OG-ZAF|ogzaf|South Africa" "og-idn|EAPD-DRB|OG-IDN|ogidn|Indonesia" "og-phl|EAPD-DRB|OG-PHL|ogphl|Philippines" + "og-bra|PSLmodels|OG-BRA|ogbra|Brazil" ) # ── Defaults ────────────────────────────────────────────────────────────────── diff --git a/scripts/repos.json b/scripts/repos.json index ae7af6c17..fa4892ef8 100644 --- a/scripts/repos.json +++ b/scripts/repos.json @@ -35,6 +35,13 @@ "repo": "OG-PHL", "package": "ogphl", "description": "Philippines" + }, + { + "key": "og-bra", + "owner": "PSLmodels", + "repo": "OG-BRA", + "package": "ogbra", + "description": "Brazil" } ] } diff --git a/tests/BENCHMARK_README.md b/tests/BENCHMARK_README.md index 892f684fb..727b8b562 100644 --- a/tests/BENCHMARK_README.md +++ b/tests/BENCHMARK_README.md @@ -87,17 +87,17 @@ Each benchmark produces a `BenchmarkResult` with the following metrics: ```python @dataclass class BenchmarkResult: - test_name: str # Name of the test - platform: str # Operating system - scheduler: str # Dask scheduler used - num_workers: int # Number of workers - compute_time: float # Execution time in seconds - peak_memory_mb: float # Peak memory usage in MB - avg_memory_mb: float # Average memory usage in MB - data_size_mb: float # Input data size in MB - num_tasks: int # Number of parallel tasks - success: bool # Whether test succeeded - error_message: str # Error details if failed + test_name: str # Name of the test + platform: str # Operating system + scheduler: str # Dask scheduler used + num_workers: int # Number of workers + compute_time: float # Execution time in seconds + peak_memory_mb: float # Peak memory usage in MB + avg_memory_mb: float # Average memory usage in MB + data_size_mb: float # Input data size in MB + num_tasks: int # Number of parallel tasks + success: bool # Whether test succeeded + error_message: str # Error details if failed ``` ### Key Metrics to Monitor @@ -228,7 +228,7 @@ cluster = LocalCluster( n_workers=num_workers, threads_per_worker=2, processes=False, # Use threads, not processes - memory_limit='4GB', + memory_limit="4GB", ) client = Client(cluster) ``` diff --git a/tests/test_SS.py b/tests/test_SS.py index a4039c059..306e08c16 100644 --- a/tests/test_SS.py +++ b/tests/test_SS.py @@ -252,7 +252,7 @@ def dask_client(): { "M": 4, "I": 4, - "io_matrix": np.eye(4), + "io_matrix": np.vstack([np.eye(4), np.eye(1, 4, 3), np.eye(1, 4, 3)]), "alpha_c": [0.1, 0.5, 0.3, 0.1], "c_min": [0.0, 0.0, 0.0, 0.0], "epsilon": [1.0, 1.0, 1.0, 1.0], @@ -418,6 +418,7 @@ def test_SS_solver(baseline, param_updates, filename, dask_client): p_m_guess, Yguess, BQguess, + p.alpha_G[-1] * Yguess, TRguess, Ig_baseline, factorguess, @@ -502,6 +503,7 @@ def test_SS_solver_extra(baseline, param_updates, filename, dask_client): p_m_guess, Yguess, BQguess, + p.alpha_G[-1] * Yguess, TRguess, Ig_baseline, factorguess, @@ -560,7 +562,7 @@ def test_solve_for_j(): param_updates7 = { "M": 4, "I": 4, - "io_matrix": np.eye(4), + "io_matrix": np.vstack([np.eye(4), np.eye(1, 4, 3), np.eye(1, 4, 3)]), "alpha_c": [0.1, 0.5, 0.3, 0.1], "c_min": [0.0, 0.0, 0.0, 0.0], "epsilon": [1.0, 1.0, 1.0, 1.0], @@ -578,6 +580,8 @@ def test_solve_for_j(): [0.25, 0.25, 0.25, 0.25], [0.1, 0.7, 0.0, 0.2], [0.0, 0.0, 1.0, 0.0], + [0.0, 0.0, 0.0, 1.0], + [0.0, 0.0, 0.0, 1.0], ] ), "alpha_c": [0.1, 0.4, 0.3, 0.1, 0.1], @@ -587,6 +591,7 @@ def test_solve_for_j(): "gamma_g": [0.0, 0.0, 0.0, 0.0], } filename8 = "inner_loop_outputs_reform_MneI.pkl" +_p_j1_tmp = Specifications() param_updates9 = { "J": 1, "lambdas": np.array([1.0]), @@ -600,6 +605,13 @@ def test_solve_for_j(): "eta": np.ones((80, 1)) * (1 / 80), "eta_RM": np.ones((80, 1)) * (1 / 80), "replacement_rate_adjust": [[1.0]], + "omega": _p_j1_tmp.omega.sum(axis=2, keepdims=True), + "omega_SS": _p_j1_tmp.omega_SS.sum(axis=1, keepdims=True), + "omega_S_preTP": _p_j1_tmp.omega_S_preTP.sum(axis=1, keepdims=True), + "rho": _p_j1_tmp.rho[:, :, :1], + "rho_preTP": _p_j1_tmp.rho_preTP[:, :1], + "imm_rates": _p_j1_tmp.imm_rates[:, :, :1], + "imm_rates_preTP": _p_j1_tmp.imm_rates_preTP[:, :1], } filename9 = "inner_loop_outputs_J1.pkl" @@ -1235,7 +1247,7 @@ def test_euler_equation_solver(input_tuple, ubi_j, p, expected): "cit_rate": [[0.21, 0.25, 0.35]], "M": 3, "I": 3, - "io_matrix": np.eye(3), + "io_matrix": np.vstack([np.eye(3), np.eye(1, 3, 2), np.eye(1, 3, 2)]), "epsilon": [1.0, 1.0, 1.0], "gamma": [0.3, 0.35, 0.4], "gamma_g": [0.1, 0.05, 0.15], @@ -1255,7 +1267,7 @@ def test_euler_equation_solver(input_tuple, ubi_j, p, expected): "cit_rate": [[0.21, 0.25, 0.35]], "M": 3, "I": 3, - "io_matrix": np.eye(3), + "io_matrix": np.vstack([np.eye(3), np.eye(1, 3, 2), np.eye(1, 3, 2)]), "epsilon": [1.0, 1.0, 1.0], "gamma": [0.3, 0.35, 0.4], "gamma_g": [0.0, 0.0, 0.0], @@ -1278,7 +1290,7 @@ def test_euler_equation_solver(input_tuple, ubi_j, p, expected): "cit_rate": [[0.21, 0.25, 0.35]], "M": 3, "I": 3, - "io_matrix": np.eye(3), + "io_matrix": np.vstack([np.eye(3), np.eye(1, 3, 2), np.eye(1, 3, 2)]), "epsilon": [1.0, 1.0, 1.0], "gamma": [0.3, 0.35, 0.4], "gamma_g": [0.0, 0.0, 0.0], diff --git a/tests/test_TPI.py b/tests/test_TPI.py index da92f8cc3..c8114bbb8 100644 --- a/tests/test_TPI.py +++ b/tests/test_TPI.py @@ -542,7 +542,7 @@ def test_inner_loop_sparse_FOC_jac(): "cit_rate": [[0.21, 0.25, 0.35]], "M": 3, "I": 3, - "io_matrix": np.eye(3), + "io_matrix": np.vstack([np.eye(3), np.eye(1, 3, 2), np.eye(1, 3, 2)]), "epsilon": [1.0, 1.0, 1.0], "gamma": [0.3, 0.35, 0.4], "gamma_g": [0.1, 0.05, 0.15], @@ -573,7 +573,7 @@ def test_inner_loop_sparse_FOC_jac(): "cit_rate": [[0.21, 0.25, 0.35]], "M": 3, "I": 3, - "io_matrix": np.eye(3), + "io_matrix": np.vstack([np.eye(3), np.eye(1, 3, 2), np.eye(1, 3, 2)]), "epsilon": [1.0, 1.0, 1.0], "gamma": [0.3, 0.35, 0.4], "gamma_g": [0.0, 0.0, 0.0], @@ -601,6 +601,8 @@ def test_inner_loop_sparse_FOC_jac(): [0.6, 0.1, 0.3], [0.25, 0.5, 0.25], [0.0, 1.0, 0.0], + [0.0, 0.0, 1.0], + [0.0, 0.0, 1.0], ] ), "epsilon": [1.0, 1.0, 1.0], @@ -630,6 +632,8 @@ def test_inner_loop_sparse_FOC_jac(): [0.6, 0.1, 0.3], [0.25, 0.5, 0.25], [0.0, 1.0, 0.0], + [0.0, 0.0, 1.0], + [0.0, 0.0, 1.0], ] ), "epsilon": [1.0, 1.0, 1.0], @@ -980,6 +984,18 @@ def test_run_TPI(baseline, param_updates, filename, tmpdir, dask_client): "eta": np.ones((40, 1)) * (1 / 40), "eta_RM": np.ones((40, 1)) * (1 / 40), "replacement_rate_adjust": [[1.0]], + # Collapse J dimension from TEST_PARAM_DICT (J=2) to J=1 + # omega arrays: sum over J (joint distribution marginal) + "omega": np.array(TEST_PARAM_DICT["omega"]).sum(-1, keepdims=True), + "omega_SS": np.array(TEST_PARAM_DICT["omega_SS"]).sum(-1, keepdims=True), + "omega_S_preTP": np.array(TEST_PARAM_DICT["omega_S_preTP"]).sum( + -1, keepdims=True + ), + # rho/imm_rates: same across J, so just take first slice + "rho": np.array(TEST_PARAM_DICT["rho"])[:, :, 0:1], + "rho_preTP": np.array(TEST_PARAM_DICT["rho_preTP"])[:, 0:1], + "imm_rates": np.array(TEST_PARAM_DICT["imm_rates"])[:, :, 0:1], + "imm_rates_preTP": np.array(TEST_PARAM_DICT["imm_rates_preTP"])[:, 0:1], } filename10 = os.path.join(CUR_PATH, "test_io_data", "run_TPI_outputs_J1.pkl") # read in mono tax funcs (not age specific) diff --git a/tests/test_aggregates.py b/tests/test_aggregates.py index 68af220cd..b0cf0e57a 100644 --- a/tests/test_aggregates.py +++ b/tests/test_aggregates.py @@ -10,7 +10,6 @@ new_param_values = { "T": 160, "S": 40, - "rho": rho_vec.tolist(), "J": 2, "chi_n": np.ones(2), "labor_income_tax_noncompliance_rate": [[0.0]], @@ -20,9 +19,17 @@ "replacement_rate_adjust": [[1.0]], "eta": (np.ones((40, 2)) / (40 * 2)), "lambdas": [0.6, 0.4], - # "omega": np.tile(np.reshape(np.ones((160, 40)) / 40, (160, 40, 1)), (1, 1, 2)) * np.array([0.6, 0.4]).tolist(), - # "omega_SS": np.tile(np.reshape(np.ones(40) / 40, (40, 1)), (1, 2)) * np.array([0.6, 0.4]).tolist(), - "e": np.ones((40, 2)), + "omega": (np.ones((160, 40)) / 40).reshape(160, 40, 1) + * np.array([0.6, 0.4]).tolist(), + "omega_SS": (np.ones(40) / 40).reshape(40, 1) + * np.array([0.6, 0.4]).tolist(), + "omega_S_preTP": (np.ones(40) / 40).reshape(40, 1) + * np.array([0.6, 0.4]).tolist(), + "imm_rates": np.zeros((160, 40, 2)).tolist(), + "imm_rates_preTP": np.zeros((40, 2)).tolist(), + "rho": np.tile(rho_vec.reshape(1, 40, 1), (160, 1, 2)).tolist(), + "rho_preTP": np.tile(rho_vec.reshape(40, 1), (1, 2)).tolist(), + "e": np.ones((40, 2)).tolist(), } # update parameters instance with new values for test p.update_specifications(new_param_values) @@ -68,7 +75,6 @@ def test_get_L_J1_regression(): "T": 160, "S": 40, "J": 1, - "rho": rho_vec_j1.tolist(), "chi_n": np.ones(1), "lambdas": [1.0], "e": np.ones((40, 1)), @@ -78,8 +84,13 @@ def test_get_L_J1_regression(): "wealth_tax_filer": [[1.0]], "replacement_rate_adjust": [[1.0]], "eta": np.ones((40, 1)) / 40, - "omega": np.ones((160, 40)) / 40, - "omega_SS": np.ones(40) / 40, + "omega": (np.ones((160, 40)) / 40).reshape(160, 40, 1).tolist(), + "omega_SS": (np.ones(40) / 40).reshape(40, 1).tolist(), + "omega_S_preTP": (np.ones(40) / 40).reshape(40, 1).tolist(), + "imm_rates": np.zeros((160, 40, 1)).tolist(), + "imm_rates_preTP": np.zeros((40, 1)).tolist(), + "rho": np.tile(rho_vec_j1.reshape(1, 40, 1), (160, 1, 1)).tolist(), + "rho_preTP": np.tile(rho_vec_j1.reshape(40, 1), (1, 1)).tolist(), } ) n = np.random.default_rng(0).random((p_j1.S, p_j1.J)) @@ -87,12 +98,7 @@ def test_get_L_J1_regression(): expected = 0.0 for s in range(p_j1.S): for j in range(p_j1.J): - expected += ( - p_j1.omega_SS[s] - * float(p_j1.lambdas[j]) - * float(p_j1.e[-1, s, j]) - * n[s, j] - ) + expected += p_j1.omega_SS[s, j] * float(p_j1.e[-1, s, j]) * n[s, j] assert np.allclose(aggr.get_L(n, p_j1, "SS"), expected) @@ -102,7 +108,6 @@ def test_get_L_J1_regression(): new_param_values = { "T": 160, "S": 40, - "rho": rho_vec.tolist(), "J": 2, "chi_n": np.ones(2), "e": np.ones((40, 2)), @@ -113,38 +118,38 @@ def test_get_L_J1_regression(): "replacement_rate_adjust": [[1.0]], "eta": (np.ones((40, 2)) / (40 * 2)), "lambdas": [0.6, 0.4], - "omega": np.ones((160, 40)) / 40, - "omega_SS": np.ones(40) / 40, - "imm_rates": np.zeros((160, 40)), + "omega": (np.ones((160, 40)) / 40).reshape(160, 40, 1) + * np.array([0.6, 0.4]).tolist(), + "omega_SS": (np.ones(40) / 40).reshape(40, 1) + * np.array([0.6, 0.4]).tolist(), + "omega_S_preTP": (np.ones(40) / 40).reshape(40, 1) + * np.array([0.6, 0.4]).tolist(), + "imm_rates": np.zeros((160, 40, 2)).tolist(), + "imm_rates_preTP": np.zeros((40, 2)).tolist(), + "rho": np.tile(rho_vec.reshape(1, 40, 1), (160, 1, 2)).tolist(), + "rho_preTP": np.tile(rho_vec.reshape(40, 1), (1, 2)).tolist(), } # update parameters instance with new values for test p.update_specifications(new_param_values) b_splus1 = 10 * np.random.rand(p.T * p.S * p.J).reshape(p.T, p.S, p.J) K_p1 = 0.9 + np.random.rand(p.T) K = 0.9 + np.random.rand(p.T) -omega_extended = np.append(p.omega_SS[1:], [0.0]) -imm_extended = np.append(p.imm_rates[-1, 1:], [0.0]) -part2 = ( - ( - b_splus1[-1, :, :] - * np.transpose((omega_extended * imm_extended) * p.lambdas) - ).sum() -) / (1 + p.g_n_ss) +omega_extended = np.append(p.omega_SS[1:, :], [[0.0, 0.0]], axis=0) +imm_extended = np.append(p.imm_rates[-1, 1:, :], [[0.0, 0.0]], axis=0) +print("Shapes:", omega_extended.shape, p.omega_SS.shape, p.omega.shape) +part2 = ((b_splus1[-1, :, :] * omega_extended * imm_extended).sum()) / ( + 1 + p.g_n_ss +) aggI_SS = (1 + p.g_n_ss) * np.exp(p.g_y) * (K_p1[-1] - part2) - ( 1.0 - p.delta ) * K[-1] -omega_shift = np.append(p.omega[: p.T, 1:], np.zeros((p.T, 1)), axis=1) -imm_shift = np.append(p.imm_rates[: p.T, 1:], np.zeros((p.T, 1)), axis=1) -part2 = ( - ( - (b_splus1 * np.squeeze(p.lambdas)) - * np.tile( - np.reshape(imm_shift * omega_shift, (p.T, p.S, 1)), (1, 1, p.J) - ) - ) - .sum(1) - .sum(1) -) / (1 + np.squeeze(np.hstack((p.g_n[: p.T - 1], p.g_n_ss)))) +omega_shift = np.append(p.omega[: p.T, 1:, :], np.zeros((p.T, 1, p.J)), axis=1) +imm_shift = np.append( + p.imm_rates[: p.T, 1:, :], np.zeros((p.T, 1, p.J)), axis=1 +) +part2 = ((b_splus1 * imm_shift * omega_shift).sum(1).sum(1)) / ( + 1 + np.squeeze(np.hstack((p.g_n[: p.T - 1], p.g_n_ss))) +) aggI_TPI = (1 + np.squeeze(np.hstack((p.g_n[: p.T - 1], p.g_n_ss)))) * np.exp( p.g_y ) * (K_p1 - part2) - (1.0 - p.delta) * K @@ -185,7 +190,6 @@ def test_get_I(b_splus1, K_p1, K, p, method, expected): new_param_values = { "T": 160, "S": 40, - "rho": rho_vec.tolist(), "J": 2, "chi_n": np.ones(2), "e": np.ones((40, 2)), @@ -196,31 +200,29 @@ def test_get_I(b_splus1, K_p1, K, p, method, expected): "replacement_rate_adjust": [[1.0]], "eta": (np.ones((40, 2)) / (40 * 2)), "lambdas": [0.6, 0.4], - "omega": np.ones((160, 40)) / 40, - "omega_SS": np.ones(40) / 40, - "imm_rates": np.zeros((160, 40)), - "omega_S_preTP": np.ones(40) / 40, - "imm_rates_preTP": np.zeros(40), - "rho_preTP": rho_vec[0, :], + "omega": (np.ones((160, 40)) / 40).reshape(160, 40, 1) + * np.array([0.6, 0.4]).tolist(), + "omega_SS": (np.ones(40) / 40).reshape(40, 1) + * np.array([0.6, 0.4]).tolist(), + "omega_S_preTP": (np.ones(40) / 40).reshape(40, 1) + * np.array([0.6, 0.4]).tolist(), + "imm_rates": np.zeros((160, 40, 2)).tolist(), + "imm_rates_preTP": np.zeros((40, 2)).tolist(), + "rho": np.tile(rho_vec.reshape(1, 40, 1), (160, 1, 2)).tolist(), + "rho_preTP": np.tile(rho_vec.reshape(40, 1), (1, 2)).tolist(), "g_n_preTP": 0.01, } # update parameters instance with new values for test p.update_specifications(new_param_values) p.omega_S_preTP = p.omega[0, :] b = -0.1 + (7 * np.random.rand(p.T * p.S * p.J).reshape(p.T, p.S, p.J)) -omega_extended = np.append(p.omega[: p.T, 1:], np.zeros((p.T, 1)), axis=1) -imm_extended = np.append(p.imm_rates[: p.T, 1:], np.zeros((p.T, 1)), axis=1) -B_test = ( - b - * np.squeeze(p.lambdas) - * np.tile(np.reshape(p.omega[: p.T, :], (p.T, p.S, 1)), (1, 1, p.J)) -) + ( - b - * np.squeeze(p.lambdas) - * np.tile( - np.reshape(omega_extended * imm_extended, (p.T, p.S, 1)), (1, 1, p.J) - ) +omega_extended = np.append( + p.omega[: p.T, 1:, :], np.zeros((p.T, 1, p.J)), axis=1 ) +imm_extended = np.append( + p.imm_rates[: p.T, 1:, :], np.zeros((p.T, 1, p.J)), axis=1 +) +B_test = (b * p.omega[: p.T, :, :]) + (b * omega_extended * imm_extended) expected1 = B_test[-1, :, :].sum() / (1.0 + p.g_n_ss) expected2 = B_test.sum(1).sum(1) / ( 1.0 + np.hstack((p.g_n[: p.T - 1], p.g_n_ss)) @@ -250,7 +252,6 @@ def test_get_B(b, p, method, PreTP, expected): new_param_values = { "T": 160, "S": 40, - "rho": rho_vec.tolist(), "J": 2, "chi_n": np.ones(2), "e": np.ones((40, 2)), @@ -261,12 +262,16 @@ def test_get_B(b, p, method, PreTP, expected): "replacement_rate_adjust": [[1.0]], "eta": (np.ones((40, 2)) / (40 * 2)), "lambdas": [0.6, 0.4], - "omega": np.ones((160, 40)) / 40, - "omega_SS": np.ones(40) / 40, - "imm_rates": np.zeros((160, 40)), - "omega_S_preTP": np.ones(40) / 40, - "imm_rates_preTP": np.zeros(40), - "rho_preTP": rho_vec[0, :], + "omega": (np.ones((160, 40)) / 40).reshape(160, 40, 1) + * np.array([0.6, 0.4]).tolist(), + "omega_SS": (np.ones(40) / 40).reshape(40, 1) + * np.array([0.6, 0.4]).tolist(), + "omega_S_preTP": (np.ones(40) / 40).reshape(40, 1) + * np.array([0.6, 0.4]).tolist(), + "imm_rates": np.zeros((160, 40, 2)).tolist(), + "imm_rates_preTP": np.zeros((40, 2)).tolist(), + "rho": np.tile(rho_vec.reshape(1, 40, 1), (160, 1, 2)).tolist(), + "rho_preTP": np.tile(rho_vec.reshape(40, 1), (1, 2)).tolist(), "g_n_preTP": 0.01, } # update parameters instance with new values for test @@ -275,10 +280,10 @@ def test_get_B(b, p, method, PreTP, expected): # set values for some variables r = 0.5 + 0.5 * np.random.rand(p.T) b_splus1 = 0.06 + 7 * np.random.rand(p.T, p.S, p.J) -pop = np.append(p.omega_S_preTP.reshape(1, p.S), p.omega[: p.T - 1, :], axis=0) -BQ_presum = (b_splus1 * np.squeeze(p.lambdas)) * np.tile( - np.reshape(p.rho[0, :] * pop, (p.T, p.S, 1)), (1, 1, p.J) +pop = np.append( + p.omega_S_preTP.reshape(1, p.S, p.J), p.omega[: p.T - 1, :, :], axis=0 ) +BQ_presum = b_splus1 * p.rho[0, :, :] * pop growth_adj = (1.0 + r) / (1.0 + np.append(p.g_n_preTP, p.g_n[: p.T - 1])) growth_adj_preTP = (1.0 + r[0]) / (1.0 + p.g_n_preTP) @@ -1209,11 +1214,11 @@ def test_get_RM(Y, p, method, expected): new_param_values = { "T": 160, "S": 40, - "rho": rho_vec.tolist(), "J": 2, "chi_n": np.ones(2), "e": np.ones((40, 2)), "M": 3, + "io_matrix": np.array([[0.0, 0.0, 1.0]] * 3), "labor_income_tax_noncompliance_rate": [[0.0]], "capital_income_tax_noncompliance_rate": [[0.0]], "income_tax_filer": [[1.0]], @@ -1221,9 +1226,16 @@ def test_get_RM(Y, p, method, expected): "replacement_rate_adjust": [[1.0]], "eta": (np.ones((40, 2)) / (40 * 2)), "lambdas": [0.6, 0.4], - "omega": np.ones((160, 40)) / 40, - "omega_SS": np.ones(40) / 40, - "imm_rates": np.zeros((160, 40)), + "omega": (np.ones((160, 40)) / 40).reshape(160, 40, 1) + * np.array([0.6, 0.4]).tolist(), + "omega_SS": (np.ones(40) / 40).reshape(40, 1) + * np.array([0.6, 0.4]).tolist(), + "omega_S_preTP": (np.ones(40) / 40).reshape(40, 1) + * np.array([0.6, 0.4]).tolist(), + "imm_rates": np.zeros((160, 40, 2)).tolist(), + "imm_rates_preTP": np.zeros((40, 2)).tolist(), + "rho": np.tile(rho_vec.reshape(1, 40, 1), (160, 1, 2)).tolist(), + "rho_preTP": np.tile(rho_vec.reshape(40, 1), (1, 2)).tolist(), } # update parameters instance with new values for test p.update_specifications(new_param_values) @@ -1231,8 +1243,8 @@ def test_get_RM(Y, p, method, expected): c = 0.1 + 0.5 * np.random.rand(p.T * p.M * p.S * p.J).reshape( p.T, p.M, p.S, p.J ) -aggC_presum = (c * np.squeeze(p.lambdas)) * np.tile( - np.reshape(p.omega[: p.T, :], (p.T, 1, p.S, 1)), (1, p.M, 1, p.J) +aggC_presum = c * np.tile( + np.reshape(p.omega[: p.T, :, :], (p.T, 1, p.S, p.J)), (1, p.M, 1, 1) ) expected1 = aggC_presum[-1, -1, :, :].sum(-1).sum(-1) expected2 = aggC_presum[:, -1, :, :].sum(-1).sum(-1) @@ -1263,7 +1275,6 @@ def test_get_C(c, p, method, expected): new_param_values = { "T": 30, "S": 20, - "rho": rho_vec.tolist(), "J": 2, "chi_n": np.ones(2), "e": np.ones((20, 2)), @@ -1283,9 +1294,16 @@ def test_get_C(c, p, method, expected): "delta_tau_annual": [ [float(1 - ((1 - 0.0975) ** (20 / (p.ending_age - p.starting_age))))] ], - "omega": np.ones((30, 20)) / 20, - "omega_SS": np.ones(20) / 20, - "imm_rates": np.zeros((30, 20)), + "omega": (np.ones((30, 20)) / 20).reshape(30, 20, 1) + * np.array([0.6, 0.4]).tolist(), + "omega_SS": (np.ones(20) / 20).reshape(20, 1) + * np.array([0.6, 0.4]).tolist(), + "omega_S_preTP": (np.ones(20) / 20).reshape(20, 1) + * np.array([0.6, 0.4]).tolist(), + "imm_rates": np.zeros((30, 20, 2)).tolist(), + "imm_rates_preTP": np.zeros((20, 2)).tolist(), + "rho": np.tile(rho_vec.reshape(1, 20, 1), (30, 1, 2)).tolist(), + "rho_preTP": np.tile(rho_vec.reshape(20, 1), (1, 2)).tolist(), } p.update_specifications(new_param_values) # make up some consumption values for testing @@ -1310,9 +1328,13 @@ def test_get_C(c, p, method, expected): # update parameters instance with new values for test p.e = 0.263 + (2.024 - 0.263) * random_state.rand(p.S * p.J).reshape(p.S, p.J) p.e = np.tile(p.e.reshape(1, p.S, p.J), (p.T, 1, 1)) -p.omega = 0.039 * random_state.rand(p.T * p.S * 1).reshape(p.T, p.S) -p.omega = p.omega / p.omega.sum(axis=1).reshape(p.T, 1) -p.omega_SS = p.omega[-1, :] +p.omega = ( + 0.039 + * random_state.rand(p.T * p.S).reshape(p.T, p.S, 1) + * np.squeeze(p.lambdas) +) +p.omega = p.omega / p.omega.sum(axis=1).sum(axis=1).reshape(p.T, 1, 1) +p.omega_SS = p.omega[-1, :, :] etr_params = 0.22 * random_state.rand( p.T * p.S * p.J * num_tax_params ).reshape(p.T, p.S, p.J, num_tax_params) @@ -1324,7 +1346,6 @@ def test_get_C(c, p, method, expected): new_param_values3 = { "T": 30, "S": 20, - "rho": rho_vec.tolist(), "J": 2, "chi_n": np.ones(2), "e": np.ones((20, 2)), @@ -1351,9 +1372,16 @@ def test_get_C(c, p, method, expected): "delta_tau_annual": [ [float(1 - ((1 - 0.0975) ** (20 / (p3.ending_age - p3.starting_age))))] ], - "omega": np.ones((30, 20)) / 20, - "omega_SS": np.ones(20) / 20, - "imm_rates": np.zeros((30, 20)), + "omega": (np.ones((30, 20)) / 20).reshape(30, 20, 1) + * np.array([0.6, 0.4]).tolist(), + "omega_SS": (np.ones(20) / 20).reshape(20, 1) + * np.array([0.6, 0.4]).tolist(), + "omega_S_preTP": (np.ones(20) / 20).reshape(20, 1) + * np.array([0.6, 0.4]).tolist(), + "imm_rates": np.zeros((30, 20, 2)).tolist(), + "imm_rates_preTP": np.zeros((20, 2)).tolist(), + "rho": np.tile(rho_vec.reshape(1, 20, 1), (30, 1, 2)).tolist(), + "rho_preTP": np.tile(rho_vec.reshape(20, 1), (1, 2)).tolist(), } p3.update_specifications(new_param_values3) p3.e = p.e @@ -1368,7 +1396,6 @@ def test_get_C(c, p, method, expected): new_param_values_ubi = { "T": 30, "S": 20, - "rho": rho_vec.tolist(), "J": 2, "chi_n": np.ones(2), "e": np.ones((20, 2)), @@ -1396,6 +1423,16 @@ def test_get_C(c, p, method, expected): "ubi_nom_017": 1000, "ubi_nom_1864": 1500, "ubi_nom_65p": 500, + "omega": (np.ones((30, 20)) / 20).reshape(30, 20, 1) + * np.array([0.6, 0.4]).tolist(), + "omega_SS": (np.ones(20) / 20).reshape(20, 1) + * np.array([0.6, 0.4]).tolist(), + "omega_S_preTP": (np.ones(20) / 20).reshape(20, 1) + * np.array([0.6, 0.4]).tolist(), + "imm_rates": np.zeros((30, 20, 2)).tolist(), + "imm_rates_preTP": np.zeros((20, 2)).tolist(), + "rho": np.tile(rho_vec.reshape(1, 20, 1), (30, 1, 2)).tolist(), + "rho_preTP": np.tile(rho_vec.reshape(20, 1), (1, 2)).tolist(), } p_u.update_specifications(new_param_values_ubi) # make up some consumption values for testing @@ -1424,9 +1461,13 @@ def test_get_C(c, p, method, expected): p.S, p.J ) p_u.e = np.tile(p_u.e.reshape(1, p_u.S, p_u.J), (p_u.T, 1, 1)) -p_u.omega = 0.039 * random_state.rand(p_u.T * p_u.S * 1).reshape(p_u.T, p_u.S) -p_u.omega = p_u.omega / p_u.omega.sum(axis=1).reshape(p_u.T, 1) -p_u.omega_SS = p_u.omega[-1, :] +p_u.omega = ( + 0.039 + * random_state.rand(p_u.T * p_u.S).reshape(p_u.T, p_u.S, 1) + * np.squeeze(p_u.lambdas) +) +p_u.omega = p_u.omega / p_u.omega.sum(axis=1).sum(axis=1).reshape(p_u.T, 1, 1) +p_u.omega_SS = p_u.omega[-1, :, :] etr_params_u = 0.22 * random_state.rand( p_u.T * p_u.S * p_u.J * num_tax_params ).reshape(p_u.T, p_u.S, p_u.J, num_tax_params) @@ -1797,7 +1838,9 @@ def test_get_r_p(r, r_gov, p_m, K_vec, K_g, D, MPKg_vec, method, expected): M = len(p_m) else: M = 1 - p.update_specifications({"T": 3, "M": M}) + io_matrix = np.zeros((p.I + 2, M)) + io_matrix[:, -1] = 1.0 + p.update_specifications({"T": 3, "M": M, "io_matrix": io_matrix}) r_p_test = aggr.get_r_p(r, r_gov, p_m, K_vec, K_g, D, MPKg_vec, p, method) @@ -1848,6 +1891,32 @@ def test_resource_constraint( assert np.allclose(test_RC, expected) +def test_get_io_prices_ss_and_tpi(): + p = Specifications() + p.I = 2 + p.M = 3 + p.io_matrix = np.array( + [ + [0.5, 0.5, 0.0], + [0.0, 0.25, 0.75], + [0.2, 0.3, 0.5], + [0.6, 0.1, 0.3], + ] + ) + p_m = np.array([2.0, 4.0, 1.0]) + p_i, p_g, p_Ig = aggr.get_io_prices(p_m, p, "SS") + assert np.allclose(p_i, [3.0, 1.75]) + assert np.isclose(p_g, 2.1) + assert np.isclose(p_Ig, 1.9) + + p_i_t, p_g_t, p_Ig_t = aggr.get_io_prices( + np.vstack([p_m, 2 * p_m]), p, "TPI" + ) + assert np.allclose(p_i_t, np.vstack([p_i, 2 * p_i])) + assert np.allclose(p_g_t, [p_g, 2 * p_g]) + assert np.allclose(p_Ig_t, [p_Ig, 2 * p_Ig]) + + def test_get_capital_outflows(): """ Test of the get_captial_outflows function. diff --git a/tests/test_basic.py b/tests/test_basic.py index 9eff476c4..4ea534cb9 100644 --- a/tests/test_basic.py +++ b/tests/test_basic.py @@ -77,9 +77,7 @@ def test_constant_demographics_TPI(tmpdir, dask_client): "PIA_rate_bkt_1": 0.0, "PIA_rate_bkt_2": 0.0, "PIA_rate_bkt_3": 0.0, - "eta": ( - spec.omega_SS.reshape(spec.S, 1) * spec.lambdas.reshape(1, spec.J) - ), + "eta": spec.omega_SS, # already (S, J) joint distribution } spec.update_specifications(og_spec) spec.etr_params = np.zeros( @@ -133,9 +131,7 @@ def test_constant_demographics_TPI_small_open(tmpdir, dask_client): "PIA_rate_bkt_1": 0.0, "PIA_rate_bkt_2": 0.0, "PIA_rate_bkt_3": 0.0, - "eta": ( - spec.omega_SS.reshape(spec.S, 1) * spec.lambdas.reshape(1, spec.J) - ), + "eta": spec.omega_SS, # already (S, J) joint distribution } spec.update_specifications(og_spec) spec.etr_params = np.zeros( diff --git a/tests/test_demographics.py b/tests/test_demographics.py index 925642ce4..14fe5fbf6 100644 --- a/tests/test_demographics.py +++ b/tests/test_demographics.py @@ -42,6 +42,7 @@ def test_get_pop_objs_read_UN_data(): T, 0, 99, + income_percentiles=[0.25, 0.25, 0.2, 0.2, 0.05, 0.04, 0.01], initial_data_year=start_year - 1, final_data_year=start_year, GraphDiag=False, @@ -71,13 +72,13 @@ def test_get_pop_objs(): infmort_rates=infmort_rates, imm_rates=imm_rates, infer_pop=True, + income_percentiles=[0.25, 0.25, 0.2, 0.2, 0.05, 0.04, 0.01], pop_dist=pop_dist[0, :].reshape(1, E + S), initial_data_year=start_year - 1, final_data_year=start_year, GraphDiag=False, ) - - assert np.allclose(pop_dict["omega_SS"], pop_dict["omega"][-1, :]) + assert np.allclose(pop_dict["omega_SS"], pop_dict["omega"][-1, :, :]) def test_pop_smooth(): @@ -100,6 +101,7 @@ def test_pop_smooth(): infmort_rates=infmort_rates, imm_rates=imm_rates, infer_pop=True, + income_percentiles=[0.25, 0.25, 0.2, 0.2, 0.05, 0.04, 0.01], pop_dist=pop_dist[0, :].reshape(1, E + S), initial_data_year=start_year - 1, final_data_year=start_year, @@ -147,6 +149,7 @@ def test_pop_growth_smooth(): infmort_rates=infmort_rates, imm_rates=imm_rates, infer_pop=True, + income_percentiles=[0.25, 0.25, 0.2, 0.2, 0.05, 0.04, 0.01], pop_dist=pop_dist[0, :].reshape(1, E + S), initial_data_year=start_year - 1, final_data_year=start_year, @@ -187,6 +190,7 @@ def test_imm_smooth(): 99, initial_data_year=start_year - 1, final_data_year=start_year, + income_percentiles=[0.25, 0.25, 0.2, 0.2, 0.05, 0.04, 0.01], GraphDiag=False, ) # assert diffs are small @@ -338,13 +342,14 @@ def test_custom_series(): T, 0, 99, + income_percentiles=[0.25, 0.25, 0.2, 0.2, 0.05, 0.04, 0.01], initial_data_year=start_year, final_data_year=start_year + 1, GraphDiag=False, imm_rates=imm_rates, infer_pop=True, ) - assert np.allclose(pop_dict["imm_rates"][0, :], imm_rates[0, E:]) + assert np.allclose(pop_dict["imm_rates"][0, :, 0], imm_rates[0, E:]) def test_custom_series_fail(): @@ -398,6 +403,7 @@ def test_custom_series_fail(): mort_rates=mort_rates, infmort_rates=infmort_rates, imm_rates=imm_rates, + income_percentiles=[0.25, 0.25, 0.2, 0.2, 0.05, 0.04, 0.01], pop_dist=pop_dist, initial_data_year=start_year, final_data_year=start_year + 1, @@ -428,6 +434,7 @@ def test_SS_dist(): imm_rates=imm_rates, infer_pop=True, pop_dist=pop_dist[0, :].reshape(1, E + S), + income_percentiles=[0.25, 0.25, 0.2, 0.2, 0.05, 0.04, 0.01], initial_data_year=start_year - 1, final_data_year=start_year, GraphDiag=False, @@ -459,6 +466,7 @@ def test_time_path_length(): infmort_rates=infmort_rates, imm_rates=imm_rates, infer_pop=True, + income_percentiles=[0.25, 0.25, 0.2, 0.2, 0.05, 0.04, 0.01], pop_dist=pop_dist[0, :].reshape(1, E + S), initial_data_year=start_year - 1, final_data_year=start_year, @@ -521,6 +529,7 @@ def test_infer_pop_nones(): mort_rates=mort_rates, infmort_rates=infmort_rates, imm_rates=imm_rates, + income_percentiles=[0.25, 0.25, 0.2, 0.2, 0.05, 0.04, 0.01], infer_pop=True, pop_dist=None, initial_data_year=start_year, @@ -548,12 +557,13 @@ def test_data_download(tmpdir): T, 0, 99, + income_percentiles=[0.25, 0.25, 0.2, 0.2, 0.05, 0.04, 0.01], initial_data_year=start_year, final_data_year=start_year + 1, download_path=tmpdir, ) - # No read in each file and call get_pop_objs again with the data + # Now read in each file and call get_pop_objs again with the data fert_rates = np.loadtxt( os.path.join(tmpdir, "fert_rates.csv"), delimiter="," ) @@ -580,6 +590,7 @@ def test_data_download(tmpdir): infmort_rates=infmort_rates[:], imm_rates=imm_rates[:, :], infer_pop=True, + income_percentiles=[0.25, 0.25, 0.2, 0.2, 0.05, 0.04, 0.01], pop_dist=pop_dist[0, :].reshape(1, E + S), initial_data_year=start_year, final_data_year=start_year + 2, @@ -602,3 +613,210 @@ def test_data_download(tmpdir): for key in pop_dict: print(key) assert np.allclose(pop_dict[key], pop_dict2[key], atol=7e-5) + + +def test_expand_pop_obj_J_tiles_when_no_income_inputs(): + """ + Test that income_percentiles expands aggregate objects by J when no + income-specific gradients or immigrant shares are supplied. + """ + E = 1 + S = 3 + J = 3 + num_periods = 5 + fixper = 2 + income_percentiles = np.array([0.5, 0.3, 0.2]) + omega_SSfx = np.array([0.2, 0.3, 0.3, 0.2]) + omega_path_lev = np.tile(omega_SSfx.reshape(1, E + S), (num_periods, 1)) + omega_path_S = omega_path_lev[:, E:] / omega_path_lev[:, E:].sum( + axis=1 + ).reshape(num_periods, 1) + fert_rates = np.zeros((num_periods, E + S)) + mort_rates = np.tile( + np.array([0.01, 0.02, 0.03, 1.0]).reshape(1, E + S), + (num_periods, 1), + ) + infmort_rates = np.ones(num_periods) * 0.005 + imm_rates = np.zeros((num_periods, E + S)) + mort_rates_S = mort_rates[:, E:] + imm_rates_mat = imm_rates[:, E:] + + pop_objs = demographics.expand_pop_obj_J( + omega_path_lev, + omega_path_S, + omega_SSfx, + fert_rates, + mort_rates, + infmort_rates, + imm_rates, + mort_rates_S, + imm_rates_mat, + E, + S, + 0.0, + fixper, + income_percentiles=income_percentiles, + ) + + assert pop_objs["omega_path_S"].shape == (num_periods, S, J) + assert np.allclose(pop_objs["omega_path_S"].sum(axis=2), omega_path_S) + assert np.allclose(pop_objs["mort_rates_S"][:, :, 0], mort_rates_S) + assert np.allclose(pop_objs["imm_rates_mat"][:, :, 0], imm_rates_mat) + assert np.allclose( + pop_objs["omega_SS"], + omega_path_S[fixper, :, None] * income_percentiles, + ) + + +def test_expand_pop_obj_J_defaults_to_single_group(): + """ + Regression test for Issue #1180: with income_percentiles=None and no + income-specific inputs (the pre-0.18 call signature), the aggregate + objects must be broadcast across a single income group rather than + raising an AssertionError. + """ + E = 1 + S = 3 + num_periods = 5 + fixper = 2 + omega_SSfx = np.array([0.2, 0.3, 0.3, 0.2]) + omega_path_lev = np.tile(omega_SSfx.reshape(1, E + S), (num_periods, 1)) + omega_path_S = omega_path_lev[:, E:] / omega_path_lev[:, E:].sum( + axis=1 + ).reshape(num_periods, 1) + fert_rates = np.zeros((num_periods, E + S)) + mort_rates = np.tile( + np.array([0.01, 0.02, 0.03, 1.0]).reshape(1, E + S), + (num_periods, 1), + ) + infmort_rates = np.ones(num_periods) * 0.005 + imm_rates = np.zeros((num_periods, E + S)) + mort_rates_S = mort_rates[:, E:] + imm_rates_mat = imm_rates[:, E:] + + pop_objs = demographics.expand_pop_obj_J( + omega_path_lev, + omega_path_S, + omega_SSfx, + fert_rates, + mort_rates, + infmort_rates, + imm_rates, + mort_rates_S, + imm_rates_mat, + E, + S, + 0.0, + fixper, + ) + + assert pop_objs["omega_path_S"].shape == (num_periods, S, 1) + assert np.allclose(pop_objs["omega_path_S"][:, :, 0], omega_path_S) + assert pop_objs["omega_SS"].shape == (S, 1) + assert np.allclose(pop_objs["mort_rates_S"][:, :, 0], mort_rates_S) + assert np.allclose(pop_objs["imm_rates_mat"][:, :, 0], imm_rates_mat) + + +def test_expand_pop_obj_J_income_inputs_require_percentiles(): + """ + income_percentiles stays required when income-specific inputs are + supplied: providing a gradient without percentiles must raise. + """ + E = 1 + S = 3 + num_periods = 5 + fixper = 2 + omega_SSfx = np.array([0.2, 0.3, 0.3, 0.2]) + omega_path_lev = np.tile(omega_SSfx.reshape(1, E + S), (num_periods, 1)) + omega_path_S = omega_path_lev[:, E:] / omega_path_lev[:, E:].sum( + axis=1 + ).reshape(num_periods, 1) + fert_rates = np.zeros((num_periods, E + S)) + mort_rates = np.tile( + np.array([0.01, 0.02, 0.03, 1.0]).reshape(1, E + S), + (num_periods, 1), + ) + infmort_rates = np.ones(num_periods) * 0.005 + imm_rates = np.zeros((num_periods, E + S)) + + with pytest.raises(AssertionError, match="income_percentiles"): + demographics.expand_pop_obj_J( + omega_path_lev, + omega_path_S, + omega_SSfx, + fert_rates, + mort_rates, + infmort_rates, + imm_rates, + mort_rates[:, E:], + imm_rates[:, E:], + E, + S, + 0.0, + fixper, + mort_gradient=np.zeros(E + S), + ) + + +def test_expand_pop_obj_J_preserves_aggregate_mortality_with_gradients(): + """ + Test that log-odds gradients generate bounded J-specific rates whose + within-age weighted means recover aggregate mortality rates. + """ + E = 1 + S = 3 + J = 3 + num_periods = 6 + fixper = 3 + g_n_ss = 0.01 + income_percentiles = np.array([0.5, 0.3, 0.2]) + omega_SSfx = np.array([0.2, 0.3, 0.3, 0.2]) + omega_path_lev = np.zeros((num_periods, E + S)) + for t in range(num_periods): + omega_path_lev[t] = 100 * ((1 + g_n_ss) ** t) * omega_SSfx + omega_path_S = omega_path_lev[:, E:] / omega_path_lev[:, E:].sum( + axis=1 + ).reshape(num_periods, 1) + omega_path_S[fixper:] = omega_path_S[fixper] + fert_rates = np.tile( + np.array([0.0, 0.02, 0.01, 0.0]).reshape(1, E + S), + (num_periods, 1), + ) + mort_rates = np.tile( + np.array([0.01, 0.02, 0.03, 1.0]).reshape(1, E + S), + (num_periods, 1), + ) + infmort_rates = np.ones(num_periods) * 0.005 + imm_rates = np.zeros((num_periods, E + S)) + mort_rates_S = mort_rates[:, E:] + imm_rates_mat = imm_rates[:, E:] + mort_gradient = np.array([-0.01, -0.005, 0.0]) + fert_gradient = np.array([0.002, 0.001, 0.0]) + + pop_objs = demographics.expand_pop_obj_J( + omega_path_lev, + omega_path_S, + omega_SSfx, + fert_rates, + mort_rates, + infmort_rates, + imm_rates, + mort_rates_S, + imm_rates_mat, + E, + S, + g_n_ss, + fixper, + income_percentiles=income_percentiles, + fert_gradient=fert_gradient, + mort_gradient=mort_gradient, + ) + + omega = pop_objs["omega_path_S"] + rho = pop_objs["mort_rates_S"] + within_age_weights = omega / omega.sum(axis=2, keepdims=True) + assert omega.shape == (num_periods, S, J) + assert rho.shape == (num_periods, S, J) + assert np.allclose(omega.sum(axis=2), omega_path_S) + assert np.allclose((within_age_weights * rho).sum(axis=2), mort_rates_S) + assert np.all((rho >= 0) & (rho <= 1)) diff --git a/tests/test_firm.py b/tests/test_firm.py index eeb376756..39e1e4f3c 100644 --- a/tests/test_firm.py +++ b/tests/test_firm.py @@ -25,8 +25,8 @@ expected3 = np.array([0.592030917]) # update parameters instance with new values for test p4 = Specifications() -rho_vec = np.zeros((1, 3)) -rho_vec[0, -1] = 1.0 +rho_vec = np.zeros((1, 3, p4.J)) +rho_vec[0, -1, :] = 1.0 new_param_values4 = { "Z": [[2.0]], "gamma": [0.5], @@ -88,6 +88,7 @@ "T": 3, "S": 3, "M": 2, + "io_matrix": np.array([[0.0, 1.0]] * 3), "chi_n": np.ones(3), "e": np.ones((3, p4.J)), "rho": rho_vec.tolist(), @@ -111,6 +112,7 @@ "T": 3, "S": 3, "M": 2, + "io_matrix": np.array([[0.0, 1.0]] * 3), "chi_n": np.ones(3), "e": np.ones((3, p4.J)), "rho": rho_vec.tolist(), @@ -130,6 +132,7 @@ "T": 3, "S": 3, "M": 3, + "io_matrix": np.array([[0.0, 0.0, 1.0]] * 3), "chi_n": np.ones(3), "e": np.ones((3, p5.J)), "rho": rho_vec.tolist(), @@ -157,6 +160,7 @@ "T": 3, "S": 3, "M": 3, + "io_matrix": np.array([[0.0, 0.0, 1.0]] * 3), "chi_n": np.ones(3), "e": np.ones((3, p5.J)), "rho": rho_vec.tolist(), @@ -821,6 +825,7 @@ def test_get_MPx(Y, x, share, p, method, expected): "c_corp_share_of_assets": 1.0, "T": 3, "M": 2, + "io_matrix": np.array([[0.0, 1.0]] * 3), "e": p3.e[0, :, :], } # update parameters instance with new values for test diff --git a/tests/test_fiscal.py b/tests/test_fiscal.py index 7d0175663..4d8012523 100644 --- a/tests/test_fiscal.py +++ b/tests/test_fiscal.py @@ -117,6 +117,8 @@ def test_D_G_path( D0_baseline = 0.59 Gbaseline[0] = 0.05 I_g = np.zeros_like(TR) + p_g = np.ones_like(TR) + p_Ig = np.ones_like(TR) net_revenue = Revenue pension_amount = np.zeros_like(net_revenue) UBI_outlays = np.zeros_like(net_revenue) @@ -127,6 +129,8 @@ def test_D_G_path( UBI_outlays, TR, I_g, + p_g, + p_Ig, Gbaseline, D0_baseline, ) @@ -205,6 +209,8 @@ def test_get_G_ss(budget_balance, expected_G): TR, UBI, I_g, + 1.0, + 1.0, new_borrowing, debt_service, p, @@ -213,6 +219,13 @@ def test_get_G_ss(budget_balance, expected_G): assert np.allclose(test_G, expected_G) +def test_get_G_ss_prices_government_goods(): + p = Specifications() + p.budget_balance = False + G = fiscal.get_G_ss(2.0, 10.0, 1.0, 2.0, 0.5, 3.0, 2.0, 4.0, 1.0, 0.5, p) + assert np.isclose(G, -2.5) + + def test_get_debt_service_f(): """ Test of fiscal.get_debt_service_f() function. @@ -272,6 +285,8 @@ def test_get_TR( agg_pension_outlays, UBI_outlays, I_g, + 1.0, + 1.0, p, method, ) diff --git a/tests/test_household.py b/tests/test_household.py index d108b4ae9..7694fd6f2 100644 --- a/tests/test_household.py +++ b/tests/test_household.py @@ -92,8 +92,10 @@ def test_marg_ut_labor(n, params, expected): p1.J = 2 p1.T = 3 p1.lambdas = np.array([0.6, 0.4]) -p1.omega_SS = np.array([0.25, 0.25, 0.5]) -p1.omega = np.tile(p1.omega_SS.reshape((1, p1.S)), (p1.T, 1)) +p1.omega_SS = np.array([0.25, 0.25, 0.5]).reshape( + (p1.S, 1) +) * p1.lambdas.reshape((1, p1.J)) +p1.omega = np.tile(p1.omega_SS.reshape((1, p1.S, p1.J)), (p1.T, 1, 1)) BQ1 = 2.5 p1.use_zeta = True expected1 = np.array([[1.66666667, 7.5], [2.5, 10.0], [0.416666667, 0.0]]) @@ -104,8 +106,10 @@ def test_marg_ut_labor(n, params, expected): p2.J = 2 p2.T = 3 p2.lambdas = np.array([0.6, 0.4]) -p2.omega_SS = np.array([0.25, 0.25, 0.5]) -p2.omega = np.tile(p2.omega_SS.reshape((1, p2.S)), (p2.T, 1)) +p2.omega_SS = np.array([0.25, 0.25, 0.5]).reshape( + (p2.S, 1) +) * p2.lambdas.reshape((1, p2.J)) +p2.omega = np.tile(p2.omega_SS.reshape((1, p2.S, p2.J)), (p2.T, 1, 1)) p2.use_zeta = True BQ2 = np.array([2.5, 0.8, 3.6]) expected2 = np.array([7.5, 10.0, 0.0]) @@ -123,8 +127,10 @@ def test_marg_ut_labor(n, params, expected): p3.J = 2 p3.T = 3 p3.lambdas = np.array([0.6, 0.4]) -p3.omega_SS = np.array([0.25, 0.25, 0.5]) -p3.omega = np.tile(p2.omega_SS.reshape((1, p2.S)), (p2.T, 1)) +p3.omega_SS = np.array([0.25, 0.25, 0.5]).reshape( + (p3.S, 1) +) * p3.lambdas.reshape((1, p3.J)) +p3.omega = np.tile(p3.omega_SS.reshape((1, p3.S, p3.J)), (p3.T, 1, 1)) p3.use_zeta = False BQ3 = np.array([1.1, 0.8]) BQ4 = np.array([[1.1, 0.8], [3.2, 4.6], [2.5, 0.1]]) @@ -182,8 +188,10 @@ def test_get_bq(BQ, j, p, method, expected): p1.J = 2 p1.T = 3 p1.lambdas = np.array([0.6, 0.4]) -p1.omega_SS = np.array([0.25, 0.25, 0.5]) -p1.omega = np.tile(p1.omega_SS.reshape((1, p1.S)), (p1.T, 1)) +p1.omega_SS = np.array([0.25, 0.25, 0.5]).reshape( + (p1.S, 1) +) * p1.lambdas.reshape((1, p1.J)) +p1.omega = np.tile(p1.omega_SS.reshape((1, p1.S, p1.J)), (p1.T, 1, 1)) TR1 = 2.5 expected1 = np.array([[1.66666667, 7.5], [2.5, 10.0], [0.416666667, 0.0]]) p2 = Specifications() @@ -196,8 +204,10 @@ def test_get_bq(BQ, j, p, method, expected): (p2.T, 1, 1), ) p2.lambdas = np.array([0.6, 0.4]) -p2.omega_SS = np.array([0.25, 0.25, 0.5]) -p2.omega = np.tile(p2.omega_SS.reshape((1, p2.S)), (p2.T, 1)) +p2.omega_SS = np.array([0.25, 0.25, 0.5]).reshape( + (p2.S, 1) +) * p2.lambdas.reshape((1, p2.J)) +p2.omega = np.tile(p2.omega_SS.reshape((1, p2.S, p2.J)), (p2.T, 1, 1)) TR2 = np.array([2.5, 0.8, 3.6]) expected2 = np.array([7.5, 10.0, 0.0]) expected3 = np.array( diff --git a/tests/test_output_plots.py b/tests/test_output_plots.py index 4be822ada..e9098224e 100644 --- a/tests/test_output_plots.py +++ b/tests/test_output_plots.py @@ -35,7 +35,7 @@ "epsilon": [0.5, 0.5, 0.5], "I": 3, "alpha_c": [0.3, 0.4, 0.3], - "io_matrix": np.eye(3), + "io_matrix": np.vstack([np.eye(3), np.eye(1, 3, 2), np.eye(1, 3, 2)]), } ) reform_params = Specifications() @@ -47,7 +47,7 @@ "epsilon": [0.5, 0.5, 0.5], "I": 3, "alpha_c": [0.3, 0.4, 0.3], - "io_matrix": np.eye(3), + "io_matrix": np.vstack([np.eye(3), np.eye(1, 3, 2), np.eye(1, 3, 2)]), } ) reform_taxfunctions = utils.safe_read_pickle( diff --git a/tests/test_output_tables.py b/tests/test_output_tables.py index f342197ba..c5dd03d26 100644 --- a/tests/test_output_tables.py +++ b/tests/test_output_tables.py @@ -33,7 +33,7 @@ "epsilon": [0.5, 0.5, 0.5], "I": 3, "alpha_c": [0.3, 0.4, 0.3], - "io_matrix": np.eye(3), + "io_matrix": np.vstack([np.eye(3), np.eye(1, 3, 2), np.eye(1, 3, 2)]), } ) reform_params = Specifications() @@ -45,7 +45,7 @@ "epsilon": [0.5, 0.5, 0.5], "I": 3, "alpha_c": [0.3, 0.4, 0.3], - "io_matrix": np.eye(3), + "io_matrix": np.vstack([np.eye(3), np.eye(1, 3, 2), np.eye(1, 3, 2)]), } ) # add investment tax credit parameter that not in cached parameters diff --git a/tests/test_parameter_plots.py b/tests/test_parameter_plots.py index c2adf31f1..809c6dea9 100644 --- a/tests/test_parameter_plots.py +++ b/tests/test_parameter_plots.py @@ -35,7 +35,7 @@ "epsilon": [0.5, 0.5, 0.5], "I": 3, "alpha_c": [0.3, 0.4, 0.3], - "io_matrix": np.eye(3), + "io_matrix": np.vstack([np.eye(3), np.eye(1, 3, 2), np.eye(1, 3, 2)]), } ) reform_params = Specifications() @@ -47,7 +47,7 @@ "epsilon": [0.5, 0.5, 0.5], "I": 3, "alpha_c": [0.3, 0.4, 0.3], - "io_matrix": np.eye(3), + "io_matrix": np.vstack([np.eye(3), np.eye(1, 3, 2), np.eye(1, 3, 2)]), } ) @@ -64,11 +64,7 @@ micro_data = utils.safe_read_pickle( os.path.join(CUR_PATH, "test_io_data", "micro_data_dict_for_tests.pkl") ) -if base_params.rho.ndim == 1: - base_params.rho = np.tile( - base_params.rho.reshape(1, base_params.S), - (base_params.T + base_params.S, 1), - ) +# rho is now (T+S, S, J) - no reshape needed def test_plot_imm_rates(): @@ -338,7 +334,11 @@ def test_plot_g_n_savefig(tmpdir): def test_plot_mort_rates_data(): totpers = base_params.S - 1 - mort_rates = base_params.rho[-1, 1:].reshape((1, totpers)) + mort_rates = ( + (base_params.rho[-1, 1:, :] * base_params.omega[-1, 1:, :]) + .mean(axis=-1) + .reshape((1, totpers)) + ) fig = parameter_plots.plot_mort_rates_data( mort_rates, path=None, @@ -349,7 +349,11 @@ def test_plot_mort_rates_data(): def test_plot_mort_rates_data_save_fig(tmpdir): totpers = base_params.S - 1 - mort_rates = base_params.rho[-1, 1:].reshape((1, totpers)) + mort_rates = ( + (base_params.rho[-1, 1:, :] * base_params.omega[-1, 1:, :]) + .mean(axis=-1) + .reshape((1, totpers)) + ) parameter_plots.plot_mort_rates_data( mort_rates, path=tmpdir, diff --git a/tests/test_parameter_tables.py b/tests/test_parameter_tables.py index c6cbbda29..c6d71330f 100644 --- a/tests/test_parameter_tables.py +++ b/tests/test_parameter_tables.py @@ -35,7 +35,7 @@ "epsilon": [0.5, 0.5, 0.5], "I": 3, "alpha_c": [0.3, 0.4, 0.3], - "io_matrix": np.eye(3), + "io_matrix": np.vstack([np.eye(3), np.eye(1, 3, 2), np.eye(1, 3, 2)]), } ) reform_params = Specifications() @@ -47,7 +47,7 @@ "epsilon": [0.5, 0.5, 0.5], "I": 3, "alpha_c": [0.3, 0.4, 0.3], - "io_matrix": np.eye(3), + "io_matrix": np.vstack([np.eye(3), np.eye(1, 3, 2), np.eye(1, 3, 2)]), } ) diff --git a/tests/test_parameters.py b/tests/test_parameters.py index 73717f768..63205beb1 100644 --- a/tests/test_parameters.py +++ b/tests/test_parameters.py @@ -53,10 +53,12 @@ def test_alpha_FA_extended_over_time_path(): assert specs.alpha_FA[-1] == 0.03 +rho_array = np.zeros((4, 3, 7)) +rho_array[:, -1, :] = 1.0 param_updates1 = { "T": 4, "S": 3, - "rho": [[0.0, 0.0, 1.0]], + "rho": rho_array.tolist(), "e": np.ones((3, 7)), "ubi_nom_017": 1000, "ubi_nom_1864": 1200, @@ -67,7 +69,7 @@ def test_alpha_FA_extended_over_time_path(): param_updates2 = { "T": 4, "S": 3, - "rho": [[0.0, 0.0, 1.0]], + "rho": rho_array.tolist(), "e": np.ones((3, 7)), "ubi_nom_017": 1000, "ubi_nom_1864": 1200, @@ -79,7 +81,7 @@ def test_alpha_FA_extended_over_time_path(): param_updates3 = { "T": 4, "S": 3, - "rho": [[0.0, 0.0, 1.0]], + "rho": rho_array.tolist(), "e": np.ones((3, 7)), "ubi_nom_017": 1000, "ubi_nom_1864": 1200, @@ -236,3 +238,28 @@ def test_J_dimensioned_length_guard(): specs3 = Specifications() with pytest.raises(ValueError, match="beta_annual"): specs3.update_specifications({"beta_annual": [0.94, 0.95, 0.96]}) + + +def test_io_matrix_shape_and_row_sums(): + specs = Specifications() + specs.update_specifications( + { + "M": 2, + "I": 2, + "io_matrix": [ + [0.5, 0.5], + [0.25, 0.75], + [0.2, 0.8], + [0.6, 0.4], + ], + } + ) + assert specs.io_matrix.shape == (4, 2) + + with pytest.raises(ValueError, match="shape"): + specs.update_specifications({"io_matrix": [[0.5, 0.5]] * 2}) + + with pytest.raises(ValueError, match="sum to 1"): + specs.update_specifications( + {"io_matrix": [[0.5, 0.5]] * 3 + [[0.2, 0.2]]} + ) diff --git a/tests/test_pensions.py b/tests/test_pensions.py index d7b3302aa..8b407da52 100644 --- a/tests/test_pensions.py +++ b/tests/test_pensions.py @@ -5,11 +5,12 @@ from ogcore.parameters import Specifications p = Specifications() -rho_vec = np.zeros((1, 4)) -rho_vec[0, -1] = 1.0 +rho_vec = np.zeros((1, 4, 1)) +rho_vec[0, -1, :] = 1.0 new_param_values = { "S": 4, "rho": rho_vec.tolist(), + "rho_preTP": rho_vec[0, :, :].tolist(), "lambdas": [1.0], "labor_income_tax_noncompliance_rate": [[0.0]], "capital_income_tax_noncompliance_rate": [[0.0]], @@ -21,6 +22,11 @@ "chi_n": np.ones(4), "eta": (np.ones((4, 1)) / (4 * 1)), "e": np.ones((4, 1)), + "omega": (np.ones((1, 4, 1)) / 4).tolist(), + "omega_SS": (np.ones((4, 1)) / 4).tolist(), + "omega_S_preTP": (np.ones((4, 1)) / 4).tolist(), + "imm_rates": np.zeros((1, 4, 1)).tolist(), + "imm_rates_preTP": np.zeros((4, 1)).tolist(), } p.update_specifications(new_param_values) p.retire = [3, 3, 3, 3, 3, 3, 3, 3] @@ -28,7 +34,8 @@ p1.update_specifications( { "S": 4, - "rho": rho_vec.tolist(), + "rho": np.tile(rho_vec, (1, 1, 2)).tolist(), + "rho_preTP": np.tile(rho_vec[0, :, :], (1, 2)).tolist(), "lambdas": [0.5, 0.5], "labor_income_tax_noncompliance_rate": [[0.0]], "capital_income_tax_noncompliance_rate": [[0.0]], @@ -40,6 +47,11 @@ "chi_n": np.ones(4), "eta": (np.ones((4, 2)) / (4 * 2)), "e": np.ones((4, 2)), + "omega": (np.ones((1, 4, 2)) / (4 * 2)).tolist(), + "omega_SS": (np.ones((4, 2)) / (4 * 2)).tolist(), + "omega_S_preTP": (np.ones((4, 2)) / (4 * 2)).tolist(), + "imm_rates": np.zeros((1, 4, 2)).tolist(), + "imm_rates_preTP": np.zeros((4, 2)).tolist(), } ) p1.retire = [3, 3, 3, 3, 3, 3, 3, 3] @@ -817,9 +829,13 @@ def test_deriv_theta(args, d_theta_expected): p2.k_ret = 0.4615 p2.mort_rates_SS = np.array([0.01, 0.05, 0.3, 0.4, 1]) p2.e = e -NDC_expected2 = np.array([0, 0.25185784, 0.24441432]) +# Pre-time-path wages are anchored to the period-0 wage of the passed +# path (w[0] = 1.1), not the old w_preTP attribute. Earlier expected +# values under the old convention, kept for reference: +# NDC_expected2 = np.array([0, 0.25185784, 0.24441432]) # TODO: why move from numbers below to those above ? Diff in numpy rounding?? # NDC_expected2 = np.array([0, 0.251721214, 0.244281728]) +NDC_expected2 = np.array([0, 0.25705613, 0.24945897]) args2 = (w, e, n, r, None, j, p2) test_data = [(args1, NDC_expected1), (args2, NDC_expected2)] @@ -935,7 +951,8 @@ def test_PS_1dim_loop(args, PS_loop_expected): ] ) p2.e = e2 -PS_expected2 = np.array([0, 0, 0.003585952, 0.003479971, 0.003377123]) +# Pre-time-path wages anchored to the period-0 wage (w2[0] = 1.21) +PS_expected2 = np.array([0, 0, 0.003852672, 0.003738809, 0.00362831]) args2 = (w2, e2, n2, j, factor, p2) test_data = [(args1, PS_expected1), (args2, PS_expected2)] @@ -952,3 +969,82 @@ def test_get_PS(args, PS_expected): PS = pensions.PS_amount(w, e, n, j, factor, p) print("PS inside of the test", PS) assert np.allclose(PS, PS_expected) + + +# Regression tests for the array/scalar bugs hit when the non-US pension +# systems run against a real Specifications object (Issues #1014 and #1075): +# p.retire is an array (time-varying since PR #433) but was passed as the +# scalar S_ret into the numba loops; p.g_y is a scalar but the loops index +# it; and in the SS the wage is a scalar but the loops index it as a path. +# The mock-parameter tests above pre-scalarize these inputs, so they never +# exercised the real interface. +@pytest.mark.parametrize( + "system,updates", + [ + ( + "Defined Benefits", + {"alpha_db": 0.02, "yr_contrib": 35, "avg_earn_num_years": 40}, + ), + ("Points System", {"vpoint": 0.5}), + # The NDC growth-rate settings (ndc_growth_rate, + # dir_growth_rate) became parameters with Issue #1169; tau_p + # must be positive for the pension to be nonzero + ( + "Notional Defined Contribution", + { + "tau_p": 0.1, + "ndc_growth_rate": "LR GDP", + "dir_growth_rate": "r", + }, + ), + ], + ids=["DB", "PS", "NDC"], +) +def test_pension_amount_with_real_specifications(system, updates): + """ + pension_amount must accept a real Specifications object in the SS: + retire is an array, g_y a scalar, and the steady-state wage a scalar. + """ + p = Specifications() + p.update_specifications(dict(updates, pension_system=system)) + j = 0 + w = 1.2 # steady-state wage is a scalar + r, Y, factor = 0.05, 1.0, 100000.0 + n = 0.4 * np.ones(p.S) + e = p.e[-1, :, j] + theta = np.zeros(p.J) + pension = pensions.pension_amount( + r, w, n, Y, theta, None, j, False, "SS", e, factor, p + ) + S_ret = int(p.retire[-1]) + assert pension.shape == (p.S,) + assert np.all(pension[:S_ret] == 0.0) + assert np.all(pension[S_ret:] > 0.0) + + +@pytest.mark.local +def test_SS_solve_defined_benefits(tmp_path): + """ + The steady state must solve with the Defined Benefits pension system + and produce positive aggregate pension outlays (Issue #1014 asked for + model-run tests of each pension system; the analogous NDC and Points + System runs should be added with the resolution of that issue). + """ + from ogcore.execute import runner + from ogcore import utils + + p = Specifications(baseline=True, num_workers=1) + p.update_specifications( + { + "pension_system": "Defined Benefits", + "alpha_db": 0.02, + "yr_contrib": 35, + "avg_earn_num_years": 40, + } + ) + p.baseline_dir = p.output_base = str(tmp_path) + runner(p, time_path=False, client=None) + ss = utils.safe_read_pickle(str(tmp_path / "SS" / "SS_vars.pkl")) + Y = np.asarray(ss["Y"]).sum() + assert ss["agg_pension_outlays"] > 0.0 + assert 0.0 < ss["agg_pension_outlays"] / Y < 1.0 diff --git a/tests/test_tax.py b/tests/test_tax.py index 918c92033..98bc990dd 100644 --- a/tests/test_tax.py +++ b/tests/test_tax.py @@ -6,17 +6,23 @@ b1 = np.array([0.1, 0.5, 0.9]) p1 = Specifications() -rho_vec = np.zeros((1, 3)) -rho_vec[0, -1] = 1.0 +rho_vec = np.zeros((1, 3, 1)) +rho_vec[0, -1, :] = 1.0 new_param_values = { "S": 3, "rho": rho_vec.tolist(), + "rho_preTP": rho_vec[0, :, :].tolist(), "lambdas": [1.0], "J": 1, "T": 3, "chi_n": np.ones(3), "e": np.ones((3, 1)), "eta": (np.ones((3, 1)) / (3 * 1)), + "omega": (np.ones((1, 3, 1)) / 3).tolist(), + "omega_SS": (np.ones((3, 1)) / 3).tolist(), + "omega_S_preTP": (np.ones((3, 1)) / 3).tolist(), + "imm_rates": np.zeros((1, 3, 1)).tolist(), + "imm_rates_preTP": np.zeros((3, 1)).tolist(), "h_wealth": [2], "p_wealth": [3], "m_wealth": [4], @@ -32,12 +38,18 @@ new_param_values2 = { "S": 3, "rho": rho_vec.tolist(), + "rho_preTP": rho_vec[0, :, :].tolist(), "lambdas": [1.0], "J": 1, "T": 3, "chi_n": np.ones(3), "e": np.ones((3, 1)), "eta": (np.ones((3, 1)) / (3 * 1)), + "omega": (np.ones((1, 3, 1)) / 3).tolist(), + "omega_SS": (np.ones((3, 1)) / 3).tolist(), + "omega_S_preTP": (np.ones((3, 1)) / 3).tolist(), + "imm_rates": np.zeros((1, 3, 1)).tolist(), + "imm_rates_preTP": np.zeros((3, 1)).tolist(), "h_wealth": [1.2, 1.1, 2.3], "p_wealth": [2.2, 2.3, 1.8], "m_wealth": [3, 4, 3], @@ -74,12 +86,18 @@ def test_ETR_wealth(b, p, expected): new_param_values = { "S": 3, "rho": rho_vec.tolist(), + "rho_preTP": rho_vec[0, :, :].tolist(), "lambdas": [1.0], "J": 1, "T": 3, "chi_n": np.ones(3), "e": np.ones((3, 1)), "eta": (np.ones((3, 1)) / (3 * 1)), + "omega": (np.ones((1, 3, 1)) / 3).tolist(), + "omega_SS": (np.ones((3, 1)) / 3).tolist(), + "omega_S_preTP": (np.ones((3, 1)) / 3).tolist(), + "imm_rates": np.zeros((1, 3, 1)).tolist(), + "imm_rates_preTP": np.zeros((3, 1)).tolist(), "h_wealth": [3], "p_wealth": [4], "m_wealth": [5], @@ -96,12 +114,18 @@ def test_ETR_wealth(b, p, expected): new_param_values2 = { "S": 3, "rho": rho_vec.tolist(), + "rho_preTP": rho_vec[0, :, :].tolist(), "lambdas": [1.0], "J": 1, "T": 3, "chi_n": np.ones(3), "e": np.ones((3, 1)), "eta": (np.ones((3, 1)) / (3 * 1)), + "omega": (np.ones((1, 3, 1)) / 3).tolist(), + "omega_SS": (np.ones((3, 1)) / 3).tolist(), + "omega_S_preTP": (np.ones((3, 1)) / 3).tolist(), + "imm_rates": np.zeros((1, 3, 1)).tolist(), + "imm_rates_preTP": np.zeros((3, 1)).tolist(), "h_wealth": [1.2, 1.1, 2.3], "p_wealth": [2.2, 2.3, 1.8], "m_wealth": [3, 4, 3], @@ -859,7 +883,13 @@ def test_MTR_income(etr_params, mtr_params, params, mtr_capital, expected): "S": 3, "chi_n": np.ones(3), "e": np.ones((3, p1.J)), - "rho": rho_vec.tolist(), + "rho": np.tile(rho_vec, (1, 1, p1.J)).tolist(), + "rho_preTP": np.tile(rho_vec[0, :, :], (1, p1.J)).tolist(), + "omega": (np.ones((1, 3, p1.J)) / (3 * p1.J)).tolist(), + "omega_SS": (np.ones((3, p1.J)) / (3 * p1.J)).tolist(), + "omega_S_preTP": (np.ones((3, p1.J)) / (3 * p1.J)).tolist(), + "imm_rates": np.zeros((1, 3, p1.J)).tolist(), + "imm_rates_preTP": np.zeros((3, p1.J)).tolist(), "eta": (np.ones((3, p1.J)) / (3 * p1.J)), "labor_income_tax_noncompliance_rate": [[0.0]], "capital_income_tax_noncompliance_rate": [[0.0]], @@ -928,8 +958,8 @@ def test_get_biz_tax(w, Y, L, K, p_m, p, m, method, expected): p.tax_func_type = "DEP" p.J = 1 p.S = 3 -p.labor_income_tax_noncompliance_rate = np.zeros((p.T, p.S, p.J)) -p.capital_income_tax_noncompliance_rate = np.zeros((p.T, p.S, p.J)) +p.labor_income_tax_noncompliance_rate = np.zeros((p.T + p.S, p.J)) +p.capital_income_tax_noncompliance_rate = np.zeros((p.T + p.S, p.J)) p.income_tax_filer = np.ones((p.T, p.J)) p.wealth_tax_filer = np.ones((p.T, p.J)) p.replacement_rate_adjust = np.ones((p.T, p.J)) @@ -945,8 +975,8 @@ def test_get_biz_tax(w, Y, L, K, p_m, p, m, method, expected): p2 = copy.deepcopy(p) p3 = copy.deepcopy(p) p3.T = 3 -p3.labor_income_tax_noncompliance_rate = np.zeros((p3.T, p3.S, p3.J)) -p3.capital_income_tax_noncompliance_rate = np.zeros((p3.T, p3.S, p3.J)) +p3.labor_income_tax_noncompliance_rate = np.zeros((p3.T + p3.S, p3.J)) +p3.capital_income_tax_noncompliance_rate = np.zeros((p3.T + p3.S, p3.J)) p3.income_tax_filer = np.ones((p3.T, p3.J)) p3.wealth_tax_filer = np.ones((p3.T, p3.J)) p3.replacement_rate_adjust = np.ones((p3.T, p3.J)) @@ -955,8 +985,8 @@ def test_get_biz_tax(w, Y, L, K, p_m, p, m, method, expected): p5.e = np.array([[0.3, 0.2], [0.5, 0.4], [0.45, 0.3]]) p5.J = 2 p5.T = 3 -p5.labor_income_tax_noncompliance_rate = np.zeros((p5.T, p5.S, p5.J)) -p5.capital_income_tax_noncompliance_rate = np.zeros((p5.T, p5.S, p5.J)) +p5.labor_income_tax_noncompliance_rate = np.zeros((p5.T + p5.S, p5.J)) +p5.capital_income_tax_noncompliance_rate = np.zeros((p5.T + p5.S, p5.J)) p5.income_tax_filer = np.ones((p5.T, p5.J)) p5.wealth_tax_filer = np.ones((p5.T, p5.J)) p5.replacement_rate_adjust = np.ones((p5.T, p5.J)) @@ -1198,7 +1228,13 @@ def test_get_biz_tax(w, Y, L, K, p_m, p, m, method, expected): "J": 2, "chi_n": np.ones(3), "e": np.ones((3, 2)), - "rho": rho_vec.tolist(), + "rho": np.tile(rho_vec, (1, 1, 2)).tolist(), + "rho_preTP": np.tile(rho_vec[0, :, :], (1, 2)).tolist(), + "omega": (np.ones((1, 3, 2)) / (3 * 2)).tolist(), + "omega_SS": (np.ones((3, 2)) / (3 * 2)).tolist(), + "omega_S_preTP": (np.ones((3, 2)) / (3 * 2)).tolist(), + "imm_rates": np.zeros((1, 3, 2)).tolist(), + "imm_rates_preTP": np.zeros((3, 2)).tolist(), "lambdas": [0.65, 0.35], "eta": (np.ones((3, 2)) / (3 * 2)), "ubi_nom_017": 1000, @@ -1796,6 +1832,108 @@ def test_net_taxes( assert np.allclose(net_taxes, expected) +def test_income_tax_liab_time_varying_noncompliance(): + # Test that time variation in the income tax noncompliance rates + # and tax filer status flows through to tax liabilities along the + # transition path + p14 = copy.deepcopy(p13) + noncompliance_path = np.array([0.0, 0.05, 0.1]) + filer_path = np.array([1.0, 1.0, 0.0]) + p14.labor_income_tax_noncompliance_rate = np.tile( + np.reshape( + np.append( + noncompliance_path, np.ones(p14.S) * noncompliance_path[-1] + ), + (p14.T + p14.S, 1), + ), + (1, p14.J), + ) + p14.capital_income_tax_noncompliance_rate = copy.deepcopy( + p14.labor_income_tax_noncompliance_rate + ) + p14.income_tax_filer = np.tile( + np.reshape( + np.append(filer_path, np.ones(p14.S) * filer_path[-1]), + (p14.T + p14.S, 1), + ), + (1, p14.J), + ) + tax_liab_path = tax.income_tax_liab( + r5, w5, b5, n5, factor, 0, None, "TPI", p5.e, etr_params5, p14 + ) + # In each period, tax liabilities along the path should match those + # computed with rates held constant at that period's values + for t in range(p14.T): + p_const = copy.deepcopy(p14) + p_const.labor_income_tax_noncompliance_rate = ( + np.ones((p14.T + p14.S, p14.J)) * noncompliance_path[t] + ) + p_const.capital_income_tax_noncompliance_rate = ( + np.ones((p14.T + p14.S, p14.J)) * noncompliance_path[t] + ) + p_const.income_tax_filer = ( + np.ones((p14.T + p14.S, p14.J)) * filer_path[t] + ) + tax_liab_const = tax.income_tax_liab( + r5, w5, b5, n5, factor, 0, None, "TPI", p5.e, etr_params5, p_const + ) + assert np.allclose(tax_liab_path[t, :, :], tax_liab_const[t, :, :]) + + +def test_income_tax_liab_time_varying_noncompliance_j(): + # Same test as above, but for tax liabilities of a single lifetime + # income group along the transition path + p_tv = copy.deepcopy(p_u) + noncompliance_path = np.array([0.0, 0.05, 0.1]) + p_tv.labor_income_tax_noncompliance_rate = np.tile( + np.reshape( + np.append( + noncompliance_path, np.ones(p_tv.S) * noncompliance_path[-1] + ), + (p_tv.T + p_tv.S, 1), + ), + (1, p_tv.J), + ) + p_tv.capital_income_tax_noncompliance_rate = copy.deepcopy( + p_tv.labor_income_tax_noncompliance_rate + ) + tax_liab_path = tax.income_tax_liab( + r11, + w11, + b11[:, :, j11], + n11[:, :, j11], + factor_u, + 0, + j11, + "TPI", + p_u.e[:, j11], + etr_params11, + p_tv, + ) + for t in range(p_tv.T): + p_const = copy.deepcopy(p_tv) + p_const.labor_income_tax_noncompliance_rate = ( + np.ones((p_tv.T + p_tv.S, p_tv.J)) * noncompliance_path[t] + ) + p_const.capital_income_tax_noncompliance_rate = ( + np.ones((p_tv.T + p_tv.S, p_tv.J)) * noncompliance_path[t] + ) + tax_liab_const = tax.income_tax_liab( + r11, + w11, + b11[:, :, j11], + n11[:, :, j11], + factor_u, + 0, + j11, + "TPI", + p_u.e[:, j11], + etr_params11, + p_const, + ) + assert np.allclose(tax_liab_path[t, :], tax_liab_const[t, :]) + + p = Specifications() p.update_specifications({"tau_c": [[0.1]]}) c = np.ones((p.T, p.I, p.S, p.J)) * 0.3 diff --git a/tests/testing_params.json b/tests/testing_params.json index 5f293c6e0..a311a3286 100644 --- a/tests/testing_params.json +++ b/tests/testing_params.json @@ -1,2357 +1,54461 @@ { -"frisch": 0.3, -"S": 40, -"J": 2, -"T": 120, -"tG1": 20, -"tG2": 80, -"lambdas": [0.6, 0.4], -"eta": [[0.0125, 0.0125], [0.0125, 0.0125], [0.0125, 0.0125], [0.0125, 0.0125], [0.0125, 0.0125], [0.0125, 0.0125], [0.0125, 0.0125], [0.0125, 0.0125], [0.0125, 0.0125], [0.0125, 0.0125], [0.0125, 0.0125], [0.0125, 0.0125], [0.0125, 0.0125], [0.0125, 0.0125], [0.0125, 0.0125], [0.0125, 0.0125], [0.0125, 0.0125], [0.0125, 0.0125], [0.0125, 0.0125], [0.0125, 0.0125], [0.0125, 0.0125], [0.0125, 0.0125], [0.0125, 0.0125], [0.0125, 0.0125], [0.0125, 0.0125], [0.0125, 0.0125], [0.0125, 0.0125], [0.0125, 0.0125], [0.0125, 0.0125], [0.0125, 0.0125], [0.0125, 0.0125], [0.0125, 0.0125], [0.0125, 0.0125], [0.0125, 0.0125], [0.0125, 0.0125], [0.0125, 0.0125], [0.0125, 0.0125], [0.0125, 0.0125], [0.0125, 0.0125], [0.0125, 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source = { editable = "." } dependencies = [ { name = "dask" }, From fd63fa8286cc6ed77892230d19f92d6401c51080 Mon Sep 17 00:00:00 2001 From: Jason DeBacker Date: Tue, 4 Aug 2026 11:19:13 -0400 Subject: [PATCH 02/14] try to guess G --- tests/test_SS.py | 3 +++ tests/test_TPI.py | 43 +++++++++++++++++++++++++++++++++++++++++++ 2 files changed, 46 insertions(+) diff --git a/tests/test_SS.py b/tests/test_SS.py index 306e08c16..01cf49e06 100644 --- a/tests/test_SS.py +++ b/tests/test_SS.py @@ -654,6 +654,7 @@ def test_inner_loop(baseline, r_p, param_updates, filename, dask_client): w = firm.get_w_from_r(r, p, "SS") TR = 0.12 Y = 1.3 + G = p.alpha_G[-1] * Y p_m = np.ones(p.M) factor = 100000 BQ = np.ones(p.J) * 0.00019646295986015257 @@ -667,6 +668,7 @@ def test_inner_loop(baseline, r_p, param_updates, filename, dask_client): p_m, Y, BQ, + G, TR, None, factor, @@ -685,6 +687,7 @@ def test_inner_loop(baseline, r_p, param_updates, filename, dask_client): p_m, Y, BQ, + G, TR, Ig_baseline, factor, diff --git a/tests/test_TPI.py b/tests/test_TPI.py index c8114bbb8..2a8d60c4f 100644 --- a/tests/test_TPI.py +++ b/tests/test_TPI.py @@ -650,6 +650,35 @@ def test_inner_loop_sparse_FOC_jac(): filename12 = os.path.join( CUR_PATH, "test_io_data", "run_TPI_baseline_MneI_cmin.pkl" ) +param_updates13 = { + "budget_balance": True, + "frisch": 0.41, + "cit_rate": [[0.21, 0.25, 0.35]], + "M": 3, + "I": 3, + "io_matrix": np.array( + [ + [0.50, 0.30, 0.20], + [0.20, 0.50, 0.30], + [0.25, 0.25, 0.50], + # Government consumption uses output from all three industries. + [0.35, 0.25, 0.40], + # Infrastructure investment uses a different industry mix. + [0.20, 0.50, 0.30], + ] + ), + "epsilon": [1.0, 1.0, 1.0], + "gamma": [0.30, 0.35, 0.40], + "gamma_g": [0.10, 0.05, 0.15], + "alpha_c": [0.20, 0.40, 0.40], + "c_min": [0.0, 0.0, 0.0], + "initial_guess_r_SS": 0.11, + "initial_guess_TR_SS": 0.07, + "alpha_G": [0.05], + "alpha_I": [0.01], + "initial_Kg_ratio": 0.01, + "debt_ratio_ss": 1.5, +} @pytest.mark.local @@ -668,6 +697,7 @@ def test_inner_loop_sparse_FOC_jac(): (True, param_updates10, filename10), (True, param_updates11, filename11), (True, param_updates12, filename12), + (True, param_updates13, None), ], ids=[ "Baseline, balanced budget", @@ -682,6 +712,7 @@ def test_inner_loop_sparse_FOC_jac(): "Baseline, M=3 zero Kg", "Baseline, M!=I", "Baseline, M!=I, cmin>0", + "Baseline, mixed government IO", ], ) def test_run_TPI_full_run( @@ -746,6 +777,18 @@ def test_run_TPI_full_run( pickle.dump(ss_outputs, f) test_dict = TPI.run_TPI(p, client=dask_client) + if filename is None: + government_io = p.io_matrix[p.I, :] + infrastructure_io = p.io_matrix[p.I + 1, :] + assert np.count_nonzero(government_io) >= 2 + assert np.count_nonzero(infrastructure_io) >= 2 + assert np.allclose(test_dict["p_g"], test_dict["p_m"] @ government_io) + assert np.allclose( + test_dict["p_Ig"], test_dict["p_m"] @ infrastructure_io + ) + max_rc_error = np.max(np.abs(test_dict["resource_constraint_error"])) + assert max_rc_error < p.RC_TPI + return expected_dict = utils.safe_read_pickle(filename) try: expected_dict["r_p"] = expected_dict.pop("r_hh") From 6a72c6b7ead65b665487d2b04b172c7e83e1260f Mon Sep 17 00:00:00 2001 From: Jason DeBacker Date: Thu, 6 Aug 2026 09:08:23 -0400 Subject: [PATCH 03/14] update inner loop outputs --- tests/test_io_data/inner_loop_outputs_J1.pkl | Bin 3174 -> 3192 bytes .../inner_loop_outputs_baseline.pkl | Bin 18437 -> 18600 bytes ...r_loop_outputs_baseline_balance_budget.pkl | Bin 18437 -> 18600 bytes ...inner_loop_outputs_baseline_small_open.pkl | Bin 18437 -> 18600 bytes .../inner_loop_outputs_reform.pkl | Bin 18423 -> 18586 bytes .../inner_loop_outputs_reform_M4.pkl | Bin 18687 -> 18682 bytes .../inner_loop_outputs_reform_MneI.pkl | Bin 18687 -> 18682 bytes ...r_loop_outputs_reform_baselinespending.pkl | Bin 18413 -> 18586 bytes 8 files changed, 0 insertions(+), 0 deletions(-) diff --git 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kjw*wkJw4_9KZPY&*Q`nvVo4{&%_(Y{V$|s{PM9LyzXMkU_S`y_Wt=#47cNr zH3ojc?0>N(cH(wYH<_4^N!Gh$9PxGN3?TIwzZf&fx{34;Z^SZ4zL7W+d%Cgxq(6B4 zFiBk}@g$JwCH9c@JHd&4<=8&bPSz8cpL|dy9OC)#%=$)pVUzz7wcs)uwO~O z;0==gIFa=ou^ZECDj_V`Y80#Nr#2!+Yim^Pr zfx#r>8|PX7Jc#8pvAyJjXa9PH=q2NTjBk>UWZaT|;SE$KnWqW;kQ_RX2p<`bWFw96 zf5LX-`G+Zk`99$GR_q5tABk}y|0|8NFdy?X&Br8uZp8Gst}#hHdq=mE5AsaBzUYr1 zi9;o(>#wtPUNEtKCK*>`yc0Xg2U1d(U(s@ion)S1kvy)!cH;i^+le=T`sXJiuL{$X zarZu(?%^*C$-IR>Q1r*=InCGaAG}fAzuxa3r*s_1!3X9mo!=Wb@(%kP?VTL<>@(kE zZ|8W-!SbNpG4p*64u2iZS$aTkEdw3)S?+;O<*2L`Su0AP{E_=@nKm8yIwzywl4ES6 Ke{Tgj&G}!PP#m)W literal 0 HcmV?d00001 From c4a02db59e567082a801e3a08404d7f4d5879b95 Mon Sep 17 00:00:00 2001 From: Jason DeBacker Date: Thu, 6 Aug 2026 09:09:38 -0400 Subject: [PATCH 05/14] add new parameterization for run_SS test --- tests/test_SS.py | 68 +++++++++++++++++++++++++++++++++++++++--------- 1 file changed, 55 insertions(+), 13 deletions(-) diff --git a/tests/test_SS.py b/tests/test_SS.py index 01cf49e06..9c47d12ca 100644 --- a/tests/test_SS.py +++ b/tests/test_SS.py @@ -617,16 +617,16 @@ def test_solve_for_j(): @pytest.mark.parametrize( - "baseline,r_p,param_updates,filename", + "baseline,r_p,G,param_updates,filename", [ - (True, 0.03309231672773741, param_updates1, filename1), - (True, 0.05, param_updates2, filename2), - (True, 0.04260341179572245, param_updates3, filename3), - (False, 0.04260341179572245, param_updates4, filename4), - (False, 0.04260341179572245, param_updates5, filename5), - (False, 0.04759112768438152, param_updates7, filename7), - (False, 0.04759112768438152, param_updates8, filename8), - (True, 0.04, param_updates9, filename9), + (True, 0.03309231672773741, 0.014437452721629856, param_updates1, filename1), + (True, 0.05, 0.0, param_updates2, filename2), + (True, 0.04260341179572245, -0.014176305891860835, param_updates3, filename3), + (False, 0.04260341179572245, -0.016740374031832955, param_updates4, filename4), + (False, 0.04260341179572245, -0.10177029059464454, param_updates5, filename5), + (False, 0.04759112768438152, 0.0, param_updates7, filename7), + (False, 0.04759112768438152, 0.0, param_updates8, filename8), + (True, 0.04, -0.1755185349329596, param_updates9, filename9), ], ids=[ "Baseline, Small Open", @@ -639,7 +639,7 @@ def test_solve_for_j(): "J=1", ], ) -def test_inner_loop(baseline, r_p, param_updates, filename, dask_client): +def test_inner_loop(baseline, r_p, G, param_updates, filename, dask_client): # Test SS.inner_loop function. Provide inputs to function and ensure that # output returned matches what it has been before. p = Specifications(baseline=baseline, num_workers=NUM_WORKERS) @@ -654,7 +654,6 @@ def test_inner_loop(baseline, r_p, param_updates, filename, dask_client): w = firm.get_w_from_r(r, p, "SS") TR = 0.12 Y = 1.3 - G = p.alpha_G[-1] * Y p_m = np.ones(p.M) factor = 100000 BQ = np.ones(p.J) * 0.00019646295986015257 @@ -1304,6 +1303,36 @@ def test_euler_equation_solver(input_tuple, ubi_j, p, expected): "debt_ratio_ss": 1.5, } filename16 = "run_SS_baseline_M3_Kg_zero_cmin.pkl" +param_updates17 = { + "budget_balance": True, + "frisch": 0.41, + "cit_rate": [[0.21, 0.25, 0.35]], + "M": 3, + "I": 3, + "io_matrix": np.array( + [ + [0.50, 0.30, 0.20], + [0.20, 0.50, 0.30], + [0.25, 0.25, 0.50], + # Government consumption uses output from all three industries. + [0.35, 0.25, 0.40], + # Infrastructure investment uses a different industry mix. + [0.20, 0.50, 0.30], + ] + ), + "epsilon": [1.0, 1.0, 1.0], + "gamma": [0.30, 0.35, 0.40], + "gamma_g": [0.10, 0.05, 0.15], + "alpha_c": [0.20, 0.40, 0.40], + "c_min": [0.0, 0.0, 0.0], + "initial_guess_r_SS": 0.11, + "initial_guess_TR_SS": 0.07, + "alpha_G": [0.05], + "alpha_I": [0.01], + "initial_Kg_ratio": 0.01, + "debt_ratio_ss": 1.5, +} +filename17 = "run_SS_baseline_mixed_government_io.pkl" # Note that changing the order in which these tests are run will cause @@ -1328,6 +1357,7 @@ def test_euler_equation_solver(input_tuple, ubi_j, p, expected): (True, param_updates14, filename14), (False, param_updates15, filename3), (True, param_updates16, filename16), + (True, param_updates17, filename17), ], ids=[ "Baseline", @@ -1346,6 +1376,7 @@ def test_euler_equation_solver(input_tuple, ubi_j, p, expected): "Baseline, M=3, zero Kg", "Reform, not use baseline solution", "Baseline, M=3, zero Kg, cmin > 0", + "Baseline, mixed government IO", ], ) @pytest.mark.local @@ -1376,6 +1407,17 @@ def test_run_SS(tmpdir, baseline, param_updates, filename, dask_client): ) p.update_specifications(param_updates) test_dict = SS.run_SS(p, client=dask_client) + if filename is None: + government_io = p.io_matrix[p.I, :] + infrastructure_io = p.io_matrix[p.I + 1, :] + assert np.count_nonzero(government_io) >= 2 + assert np.count_nonzero(infrastructure_io) >= 2 + assert np.isclose(test_dict["p_g"], test_dict["p_m"] @ government_io) + assert np.isclose( + test_dict["p_Ig"], test_dict["p_m"] @ infrastructure_io + ) + assert np.max(np.abs(test_dict["resource_constraint_error"])) < p.RC_SS + return expected_dict = utils.safe_read_pickle( os.path.join(CUR_PATH, "test_io_data", filename) ) @@ -1412,8 +1454,8 @@ def test_initial_guesses(tmpdir, use_zeta): guesses, n_guess, b_guess = SS.SS_initial_guesses(p) if use_zeta: - assert len(guesses) == 7 + 1 + assert len(guesses) == 8 + 1 else: - assert len(guesses) == 7 + p.J + assert len(guesses) == 8 + p.J assert n_guess.shape == (p.S, p.J) assert b_guess.shape == (p.S, p.J) From 7ce5c1ddbdb45052ada0ec0693a633b26c474712 Mon Sep 17 00:00:00 2001 From: Jason DeBacker Date: Thu, 6 Aug 2026 09:35:30 -0400 Subject: [PATCH 06/14] add new param to test with full tpi solution --- tests/test_TPI.py | 18 +++++------------- 1 file changed, 5 insertions(+), 13 deletions(-) diff --git a/tests/test_TPI.py b/tests/test_TPI.py index 2a8d60c4f..b38a5ba3f 100644 --- a/tests/test_TPI.py +++ b/tests/test_TPI.py @@ -679,6 +679,9 @@ def test_inner_loop_sparse_FOC_jac(): "initial_Kg_ratio": 0.01, "debt_ratio_ss": 1.5, } +filename13 = os.path.join( + CUR_PATH, "test_io_data", "run_TPI_baseline_mixed_IO.pkl" +) @pytest.mark.local @@ -697,7 +700,7 @@ def test_inner_loop_sparse_FOC_jac(): (True, param_updates10, filename10), (True, param_updates11, filename11), (True, param_updates12, filename12), - (True, param_updates13, None), + (True, param_updates13, filename13), ], ids=[ "Baseline, balanced budget", @@ -777,18 +780,7 @@ def test_run_TPI_full_run( pickle.dump(ss_outputs, f) test_dict = TPI.run_TPI(p, client=dask_client) - if filename is None: - government_io = p.io_matrix[p.I, :] - infrastructure_io = p.io_matrix[p.I + 1, :] - assert np.count_nonzero(government_io) >= 2 - assert np.count_nonzero(infrastructure_io) >= 2 - assert np.allclose(test_dict["p_g"], test_dict["p_m"] @ government_io) - assert np.allclose( - test_dict["p_Ig"], test_dict["p_m"] @ infrastructure_io - ) - max_rc_error = np.max(np.abs(test_dict["resource_constraint_error"])) - assert max_rc_error < p.RC_TPI - return + expected_dict = utils.safe_read_pickle(filename) try: expected_dict["r_p"] = expected_dict.pop("r_hh") From 08dd8f4e9a2da94b50ab35a0185b158548fc36dc Mon Sep 17 00:00:00 2001 From: Jason DeBacker Date: Thu, 6 Aug 2026 09:36:19 -0400 Subject: [PATCH 07/14] add test data for new tpi run parameterization --- .../run_TPI_baseline_mixed_IO.pkl | Bin 0 -> 36020332 bytes 1 file changed, 0 insertions(+), 0 deletions(-) create mode 100644 tests/test_io_data/run_TPI_baseline_mixed_IO.pkl diff --git a/tests/test_io_data/run_TPI_baseline_mixed_IO.pkl b/tests/test_io_data/run_TPI_baseline_mixed_IO.pkl new file mode 100644 index 0000000000000000000000000000000000000000..2e3f9b8929c14a31071f71678c82ea18360a97c4 GIT binary patch literal 36020332 zcmeF(=|5Fb`vCmoIHr)PGNeL?B10*atZhijkfe;6LNX_rN*T(W6e%J@iBe{fh;WR@ zOpd8g9ApkjhUeP%{drzIuYP~PbN8a}XI*QpYp=b|-e+Igl|Sjp1S1^*|K%4e5lLqp z8@a>frn8&RL5nM{?zRV=Z#sEgvvPO0@`;UPvv9Y);_7n4!~NzJkJy;lNQVE6jE!V+ zvH9)4Q3{a6VtIvtGeeylx1B!i8IkDDz%hiIo58y5QCZu&?%rC42E z-LsqU>p#C~5js3aOKvD93x9iZp zVRq3bc?8K5dC65xBj`ixWsc`dpmg=ku3bOSHEtii4)q_%<7E-|dxsw=&o}f|(8C`{ zWFcVto`N4p0J|NP*!=^!wbcZu|M`J}Ubtl(;QNVG|6Q`SQ2dE@zfK@LGW?0OJIzJj z-~5S=ya;w?iTR22|47+&=ln!09s&Qxn|>m;BF3~cw4dnpY{czux=}mZD9R80#XOuoij2&Xr`SqI z5#8s#i8jrnXh>nN{?vEqkJlY|IX{ZxX$PIsiDO7#{JQEko-y>|d7o~V$QW9t^y?4G zjiJm4yBs-e4Bft55ku1%LoXM_Qr1ky&|G&=Rp`|*#O*HjH}%FCB8}TqH13X}(mT7N zy~4+kQP(j{^~o4&Y?s7JlE=`~?cUU(H)ANmut}!4U<~m$6_n_gk0G26L-sR~C+8=|P$4g)@6LrW#BnI5e)aDda#5+bOr)cs z^NP{li_A0>#GN|A#7RTmcm96s*+D}o`(u5rcG1uqeX4xUUK*+yRoGW3NkjdX`XyIo zXh^k^`FYb}8rs4qq5SI@4OOesZqzH&(BmI=PwdoS9Q)(4meVxE_-980gEkF)T}|4} zp+`fz-;*}xFVIke0iW#~LmFDsY)qCnrJ>d0Jp<3oX~x&hq`Ex4&Oh1PXcL3N>*xr-#r={`c=@t5<)}!-td_34Wl7y zamU3pm|J1p`UTys8|JlF6sLm( z7bhXH95^5MJ0-~=!-1mkpBX9mFCRotY*1+Xu>5{5&-fqy~iRM7r}p|f})4K>ynUAztwp1<_|2tF)V zp5Fkcie;%PMbO_DeJT)qdMr+_5jP6Ui&&@DcqNl z2O9aoHYrhODbQeJ+)^1_shG+-2R6>x7+8WdacxUC&`igs`z|QdsQ57oBtH=1N&?&1 zv5~i6?fwY45-^8YM^6SV^CB*{gNmD{)CR%!4aJY+;M;_|)xSYLo~?EpV48EmE9Or$ zRPt3Ih#TCoOd$z^nuq12#XyBjB>jr)*`g??})i4KS%falbD3c5m#h zi=gY|p-?lB^KX*=74WbxHh2xxD0koQ2EJnVRPqGd13b6i2B)ebiUYvi%^yWWz>Lg! z^GNWDAh-P^Fw}TlB@Wb1m7PfdkNk{tNd~)2i)&tjal7{{WPoPb+ACRLZa^+I2mBlI zKDGet%Z!&U1}Vy)UzLKY4~+hmgP!sU@?XF|v2V0$Kujm?bRDP>@nl~k80Z~7MFB

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