From c98034e396f59ed6d9b5f2b1950050ef07baf79a Mon Sep 17 00:00:00 2001 From: jdebacker Date: Sun, 28 Jul 2024 21:47:07 -0400 Subject: [PATCH 01/30] add script to make images for book --- ogzaf/docs_tables_figures.py | 187 +++++++++++++++++++++++++++++++++++ 1 file changed, 187 insertions(+) create mode 100644 ogzaf/docs_tables_figures.py diff --git a/ogzaf/docs_tables_figures.py b/ogzaf/docs_tables_figures.py new file mode 100644 index 0000000..56396be --- /dev/null +++ b/ogzaf/docs_tables_figures.py @@ -0,0 +1,187 @@ +""" +This script creates tables and figures from the OG-ZAF documentation. +""" + +# import +import os +import pandas as pd +import numpy as np +import matplotlib.pyplot as plt +import json +import ogzaf +from ogcore.parameters import Specifications +from ogcore import parameter_plots as pp +from ogcore import demographics + +CUR_DIR = os.path.dirname(os.path.realpath(__file__)) +UN_COUNTRY_CODE = "710" +plot_path = os.path.join(CUR_DIR, "..", "docs", "book", "content", "calibration", "images") +YEAR_TO_PLOT = 2025 + +# Use a custom matplotlib style file for plots +style_file_url = ( + "https://raw.githubusercontent.com/PSLmodels/OG-Core/" + + "master/ogcore/OGcorePlots.mplstyle" +) +plt.style.use(style_file_url) + +""" +Load specifications object with default parameters +""" +p = Specifications() +p.update_specifications( + json.load( + open( + os.path.join( + CUR_DIR, "..", "ogzaf", "ogzaf_default_parameters.json" # TODO: change to URL + ) + ) + ) + ) + +# also load parameters from OG-USA for comparison +p2 = Specifications() +p2.update_specifications( + json.load( + open( + os.path.join( + CUR_DIR, "..", "..", "OG-USA", "ogusa", "ogusa_default_parameters.json" # TODO: change to URL + ) + ) + ) + ) + +""" +Demographics chapter +""" +# Fertility rates +# pp.plot_fert_rates( +# [fert_rates_2D], +# start_year=start_year, +# years_to_plot=[start_year, end_year], +# path=plot_path, +# ) +# or +demographics.get_fert( + totpers=100, + min_age=0, + max_age=99, + country_id=UN_COUNTRY_CODE, + start_year=YEAR_TO_PLOT, + end_year=YEAR_TO_PLOT, + graph=True, + plot_path=plot_path, + download_path=None, +) +# Mortality rates +# pp.plot_mort_rates_data( +# mort_rates_2D, +# start_year, +# [start_year, end_year], +# path=plot_path, +# ) +demographics.get_mort( + totpers=100, + min_age=0, + max_age=99, + country_id=UN_COUNTRY_CODE, + start_year=YEAR_TO_PLOT, + end_year=YEAR_TO_PLOT, + graph=True, + plot_path=plot_path, + download_path=None, +) +# Immigration rates +# pp.plot_imm_rates( +# imm_rates_2D, +# start_year, +# [start_year, end_year], +# path=plot_path, +# ) +demographics.get_imm_rates( + totpers=100, + min_age=0, + max_age=99, + fert_rates=None, + mort_rates=None, + infmort_rates=None, + pop_dist=None, + country_id=UN_COUNTRY_CODE, + start_year=YEAR_TO_PLOT, + end_year=YEAR_TO_PLOT, + graph=True, + plot_path=plot_path, + download_path=None, +) +# Fixed versus original population distribution +# pp.plot_omega_fixed( +# age_per_EpS, omega_SS_orig, omega_SSfx, E, S, path=OUTPUT_DIR +# ) +# Fixed versus adjusted immigration rates +# pp.plot_imm_fixed( +# age_per_EpS, +# imm_rates_orig[fixper - 1, :], +# imm_rates_adj, +# E, +# S, +# path=OUTPUT_DIR, +# ) +demographics.get_pop_objs( + E=20, + S=80, + T=320, + min_age=0, + max_age=99, + fert_rates=None, + mort_rates=None, + infmort_rates=None, + imm_rates=None, + infer_pop=False, + pop_dist=None, + pre_pop_dist=None, + country_id=UN_COUNTRY_CODE, + initial_data_year=YEAR_TO_PLOT - 1, + final_data_year=YEAR_TO_PLOT + 2, + GraphDiag=True, + download_path=None, +) +# Population distribution at different points in time +# pp.plot_population_path( +# age_per_EpS, +# omega_path_lev, +# omega_SSfx, +# initial_data_year, +# initial_data_year, +# initial_data_year, +# S, +# path=OUTPUT_DIR, +# ) +pp.plot_population(p, years_to_plot=[YEAR_TO_PLOT, YEAR_TO_PLOT + 25, YEAR_TO_PLOT + 50, YEAR_TO_PLOT + 100], include_title=False, path=plot_path) +# Population growth # want average growth rate with this +pp.plot_pop_growth( + p, + start_year=YEAR_TO_PLOT, + num_years_to_plot=150, + include_title=False, + path=plot_path, +) +""" +Income chapter +""" +pp.plot_ability_profiles( + p, p2=None, t=None, log_scale=True, include_title=False, path=plot_path +) +# Plotting with USA also is too busy + + +# * Fert rates + # * Mort rates -- should we use survival function? + # * Imm rates + # * SS pop vs fixed ** do we do this? ** + # * Adj vs not imm rates ** do we do this? ** + # * Pop dist at points in time + # * Pop growth rate over time with avg rate + # * Life time earning profiles + # * Prob also want US for comparison + # * Note gini in text + # * Drop tables from income chapter \ No newline at end of file From 27bde2f1f49d8220e1bb6cc9ef0e300fec4b1055 Mon Sep 17 00:00:00 2001 From: jdebacker Date: Mon, 29 Jul 2024 17:36:59 -0400 Subject: [PATCH 02/30] remove list of ogusa cites --- docs/book/citations.bib | 207 ---------------------------------------- 1 file changed, 207 deletions(-) diff --git a/docs/book/citations.bib b/docs/book/citations.bib index 4030e56..e69de29 100644 --- a/docs/book/citations.bib +++ b/docs/book/citations.bib @@ -1,207 +0,0 @@ -@TECHREPORT{DeBackerEtAl:2017b, - AUTHOR = {Jason DeBacker and Richard W. Evans and Evan Magnusson and Kerk L. Phillips and Shanthi Ramnath and Isaac Swift}, - TITLE = {The Distributional Effects of Redistributional Tax Policy}, - INSTITUTION = {Open Source Macroeconomics Laboratory}, - YEAR = {2017b}, - type = {mimeo}, - month = {January}, -} - -@Article{DEP:2019, - author={Jason DeBacker and Richard W. Evans and Kerk L. Phillips}, - title={{Integrating Microsimulation Models of Tax Policy into a DGE Macroeconomic Model}}, - journal={Public Finance Review}, - year=2019, - volume={47}, - number={2}, - pages={207-275}, - month={March}, - keywords={microsimulation; effective tax rates; marginal tax rates; dynamic general equilibrium; dynamic scori}, - doi={}, - abstract={This article proposes a method for integrating individual effective tax rates and marginal tax rates computed from a microsimulation (partial equilibrium) model of tax policy with a dynamic general equilibrium model of tax policy that can provide macroeconomic analysis or dynamic scores of tax reforms. Our approach captures the rich heterogeneity, realistic demographics, and tax-code detail of the microsimulation model and allows this detail to inform a general equilibrium model with a relatively high degree of heterogeneity. In addition, we propose a functional form in which tax rates depend jointly on the levels of both capital income and labor income.}, - url={https://ideas.repec.org/a/sae/pubfin/v47y2019i2p207-275.html} -} - -@TECHREPORT{Pomerleau2020b, - AUTHOR = {Kyle Pomerleau}, - TITLE = {An analysis of Joe Biden’s tax proposals, October 2020 update}, - INSTITUTION = {American Enterprise Institute}, - YEAR = {2020}, - type = {AEI Report}, - month = {October}, - url = {https://www.aei.org/research-products/report/an-analysis-of-joe-bidens-tax-proposals-october-2020-update/} -} - -@TECHREPORT{DEP2020, - AUTHOR = {Jason DeBacker and Richard Evans and Kyle Pomerleau}, - TITLE = {An analysis of Joe Biden’s tax proposals}, - INSTITUTION = {American Enterprise Institute}, - YEAR = {2020}, - type = {AEI Report}, - month = {June}, - url = {https://www.aei.org/research-products/report/an-analysis-of-joe-bidens-tax-proposals/} -} - -@Article{DeBackerFrailey:2019, - author={Jason DeBacker and Anderson Frailey}, - title={Revenue and Macroeconomic Effects of a 70% Marginal Tax Rate}, - journal={Quantitative Notes}, - year=2019, - volume={}, - number={2019-1}, - pages={}, - month={March}, - keywords={}, - doi={}, - abstract={Recently, there has been considerable dis- cussion of a significant increase in the top marginal income tax rate. A salient top marginal tax rate is 70%. This note simulates the effects of a 70% top rate on different groups of filers and shows the im- pacts on revenue and macroeconomic aggregates. We find that an increase in the top marginal tax rate to 70% raises between $5 billion and $250 billion per year over the first 10 years, depending on the size of the top bracket to which this rate is applied. However, our macroeconomic simulations show that a 70% top rate lowers GDP by between 1.7% and 0.1% in the near term, although there may be posi- tive effects on GDP in the longer term.}, - url={https://www.openrg.com/reports/70pctMTR_QN.pdf} -} - -@Article{Evans:2018, - author={Richard W. Evans}, - title={Dynamic Analysis of EITC Expansion}, - journal={Quantitative Notes}, - year=2018, - volume={}, - number={2018-2}, - pages={}, - month={May}, - keywords={}, - doi={}, - abstract={This Quantitative Note uses the OG-USA open source dynamic general equilibrium overlap- ping generations model to perform a dynamic analy- sis of the Brown-Khanna Grow American Incomes Now (GAIN) Act, which proposes to increase the generosity and scope of the earned income tax credit (EITC) in the United States. I show a simulation of the macroeconomic effects as well as distributional analysis resulting from the GAIN Act. I also sim- ulate the effects of a revenue neutral GAIN Act in which an increase in the marginal income tax rates in the top two personal income brackets exactly off- sets the reduction in total federal tax revenue from the EITC expansion. In the case of the GAIN Act alone, the economy experiences short-run gains, but the increased government debt quickly crowds out in- vestment and causes the economy to start shrinking significantly. In the revenue neutral case, the cost is primarily in terms of large labor supply frictions and a reallocation of the household labor-leisure and consumption-savings decisions.}, - url={https://www.openrg.com/reports/QN_EITC_v1.1.pdf} -} - -@Article{DeBackerEvans:2018, - author={Jason DeBacker and Richard W. Evans}, - title={Dynamic Analysis of Tax Cuts and Jobs Act}, - journal={Quantitative Notes}, - year=2018, - volume={}, - number={2018-1}, - pages={}, - month={February}, - keywords={}, - doi={}, - abstract={This Quantitative Note uses the OG-USA open source dynamic general equilibrium overlap- ping generations model to simulate the effect of the Tax Cuts and Jobs Act. We simulate this reform under the assumptions of a closed economy and small open economy. In both cases, the TCJA reform causes significant growth in GDP and employment between 1% and 2% per year in the first 8 years. However, the increasing debt-to-GDP ratio quickly crowds out investment and causes a drag on the economy. Wage growth can range from nearly nonexistent to a mod- est 0.6%, depending critically on the assumption of how much capital will flow into the country.}, - url={https://www.openrg.com/reports/QN_ogusa_TCJA.pdf} -} - -@Article{Evans:2017, - author={Richard W. Evans}, - title={Dynamic Analysis of Corporate Income Tax Rate Cut}, - journal={Quantitative Notes}, - year=2017, - volume={}, - number={2017-4}, - pages={}, - month={November}, - keywords={}, - doi={}, - abstract={This Quantitative Note uses the OG-USA open source dynamic general equilibrium overlap- ping generations model to simulate the effect of cut- ting the U.S. corporate income tax rate from 35% to 20%. I simulate this rate cut under the assump- tions of a closed economy and small open economy, respectively. In both cases, the corporate rate cut causes government revenues to decrease and the debt-to-GDP ratio to increase. In the small open economy scenario, GDP and wages increase by around 3.0%, and 2.5%, respectively. However, in the closed economy setting in which the increased debt service must be satisfied by domestic savings (crowding out), the GDP and wage gains are much smaller and short lived.}, - url={https://www.openrg.com/reports/QN_CorpCut.pdf} -} - -@TECHREPORT{DEP:2015, - AUTHOR = {Jason DeBacker and Richard W. Evans and Kerk L. Phillips}, - TITLE = {Macroeconomic effects of a 10% cut in statutory marginal income tax rates on ordinary income}, - INSTITUTION = {American Enterprise Institute}, - YEAR = {2015}, - type = {AEI Economic Policy Working Paper Series}, - month = {December}, -} - -@Article{Ferenstein:2018, - author={Gregory Ferenstein}, - title={Can The U.S. Afford A Massive Wage Subsidy? A Macroeconomic Simulation}, - journal={Forbes}, - year=2018, - volume={}, - number={}, - pages={}, - month={September}, - keywords={}, - doi={}, - abstract={}, - url={https://www.forbes.com/sites/gregoryferenstein/2018/09/30/can-the-us-afford-a-massive-wage-subsidy-a-macroeconomic-simulation/#614ea9032502} -} - -@TECHREPORT{MichelFurth:2017b, - AUTHOR = {Adam Michel and Salim Furth}, - TITLE = {For Pro-Growth Tax Reform, Expensing Should Be the Focus}, - INSTITUTION = {The Heritage Foundation}, - YEAR = {2017}, - type = {Taxes Report}, - month = {August}, - url = {https://www.heritage.org/taxes/report/pro-growth-tax-reform-expensing-should-be-the-focus} -} - -@TECHREPORT{MichelFurth:2017a, - AUTHOR = {Norbert Michel and Salim Furth}, - TITLE = {The Macroeconomic Impact of Dodd Frank—and of Its Repeal}, - INSTITUTION = {The Heritage Foundation}, - YEAR = {2017}, - type = {Taxes Report}, - month = {April}, - url = {https://www.heritage.org/markets-and-finance/report/the-macroeconomic-impact-dodd-frank-and-its-repeal} -} - -@TECHREPORT{Hassett:2015, - AUTHOR = {Kevin A. Hassett}, - TITLE = {On the Dynamic Scoring of Fiscal Policy}, - INSTITUTION = {}, - YEAR = {2015}, - type = {Congressional Testimony}, - month = {July}, - url = {https://www.aei.org/wp-content/uploads/2015/07/Hassett_DynamicScoring_final-00000002.pdf} -} - -@TECHREPORT{Hassett:2016, - AUTHOR = {Kevin A. Hassett}, - TITLE = {Statement before the House Ways and Means Committee: Reaching America’s Potential: Delivering Growth and Opportunity for All Americans}, - INSTITUTION = {}, - YEAR = {2016}, - type = {Congressional Testimony}, - month = {February}, - url = {https://www.aei.org/wp-content/uploads/2016/02/KHtestimony.pdf} -} - -@TECHREPORT{DEP:2021a, - AUTHOR = {Jason DeBacker and Richard W. Evans and Benjamin R. Page}, - TITLE = {A Detailed Macroeconomic Analysis of President Biden's 2020 Campaign Tax Proposals}, - INSTITUTION = {Tax Policy Center}, - YEAR = {2021}, - type = {Working Paper}, - month = {July}, - url = {https://www.taxpolicycenter.org/publications/detailed-macroeconomic-analysis-president-bidens-2020-campaign-tax-proposals} -} - -@TECHREPORT{DEP:2021b, - AUTHOR = {Jason DeBacker and Richard W. Evans and Benjamin R. Page}, - TITLE = {A Sensitivity Analysis of a Detailed Macroeconomic Analysis of President Biden's 2020 Campaign Tax Proposals}, - INSTITUTION = {Tax Policy Center}, - YEAR = {2021}, - type = {Working Paper}, - month = {July}, - url = {https://www.taxpolicycenter.org/publications/sensitivity-analysis-detailed-macroeconomic-analysis-president-bidens-2020-campaign-tax} -} - -@TECHREPORT{DEP:2021c, - AUTHOR = {Benjamin R. Page and Jeffrey Rohaly and Thornton Matheson and Gordon B. Mermin and Jason DeBacker and Richard W. Evans}, - TITLE = {Macroeconomic Analysis of Former Vice President Biden's Tax Proposals}, - INSTITUTION = {Tax Policy Center}, - YEAR = {2021}, - type = {Brief}, - month = {July}, - url = {https://www.taxpolicycenter.org/publications/macroeconomic-analysis-former-vice-president-bidens-tax-proposals} -} - -@TECHREPORT{Page:2021, - AUTHOR = {Benjamin R. Page}, - TITLE = {TPC Experiments with Another Model to Estimate the Economic Effects of Tax Law Changes}, - INSTITUTION = {Tax Policy Center}, - YEAR = {2021}, - type = {TaxVox: Campaigns, Proposals, and Reforms}, - month = {July}, - url = {https://www.taxpolicycenter.org/taxvox/tpc-experiments-another-model-estimate-economic-effects-tax-law-changes} -} From 6d0df32b308aece3118bb50c461e1d0677d52eee Mon Sep 17 00:00:00 2001 From: jdebacker Date: Mon, 29 Jul 2024 17:37:07 -0400 Subject: [PATCH 03/30] update fert rates pictures --- .../content/calibration/images/fert_rates.png | Bin 53588 -> 133136 bytes .../content/calibration/images/mort_rates.png | Bin 40562 -> 123794 bytes 2 files changed, 0 insertions(+), 0 deletions(-) diff --git a/docs/book/content/calibration/images/fert_rates.png b/docs/book/content/calibration/images/fert_rates.png index 46a0c4ebb43810dd88894f685c359e1cabb20f79..4af30e2d8411a673f13bd2e3f463e759eea940b1 100644 GIT binary patch literal 133136 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z5=4CW=Nr-L>+5sNx>Eq0G8u~#EL07H&=6%~0%ZYLy!;CKo~ zUB`cmkdc$Oo2Np^gc{Ezl$M@;#>PfJ%@<>4;-P8>GfSx9Bbv2_O;RrhgyZQcg)1W5@olsimhJzcYy0|Gh$Zn`T{v!H zVgd}##*j$aVQmSu87RIH7lo7r0u2l}Wh#Hy{)|pe-UR5eys{GWJMq%YtHT-^bRcqU z!bW-d_;C61mS3Jborm$oE3d>c{;|Bna(TC3=GFZeGewCQrV-iN2=JBppGZsuX<9JR zBNRJLh`SI7B`Wi-z>-L68bVl@wr}48^d7(hISEYVO%&q^r-CJw@H#sC?x_&6$pAz* zHA6$&G3F=#6@w4!9~BSe^n?QhBqDmIjv|QV+t{U(7_~tIITRC9Q{)t6v9Ylb0y=u* z>io~2%}`*QN6t|P>;h (|LG-=oJ=qj(}MKR76i?&Nrax1bi zyO1hGfZU{{r1L+1+|1A4k1$4W<~b!bwG9r|r#FQ=koJvy{u~CEE--;9k-A#pVHLQ= zMtLTI0I}+^_Q*WmrrbbI8-h4L4ekYCbUmgKT?14oFaaKznDGAnOq`sdus^lEz04S1 zeI>I!?9ptQpiB5xTlnFkqN2?zXM)@9C#)jW}vK?p7s7CC^v_4N;8C9UjkV<_tccg{!UGy z)G~&#BJ5I*CJCQ0Xj={$F)6B%RL$3*9dZU|gK$eY%c8!^uEgxke=MsN5f18(vKEj1 z5pnr#G;!ILs6T_VNC)!8r}w1?02+s53vuu^O@sZq4Wcx0|1E={qVj*L cr>rQIkWaneTk10ht#* Date: Mon, 29 Jul 2024 23:22:53 -0400 Subject: [PATCH 04/30] remove demog api doc --- docs/book/content/api/demographics.rst | 15 --------------- 1 file changed, 15 deletions(-) delete mode 100644 docs/book/content/api/demographics.rst diff --git a/docs/book/content/api/demographics.rst b/docs/book/content/api/demographics.rst deleted file mode 100644 index d851109..0000000 --- a/docs/book/content/api/demographics.rst +++ /dev/null @@ -1,15 +0,0 @@ -.. _demographics: - -Demographics Functions -======================== - -**demographics.py modules** - -ogzaf.demographics ------------------------------------------- - -.. automodule:: ogzaf.demographics - :members: get_un_fert_data, get_un_mort_data, get_wb_infmort_rate, - get_un_pop_data, get_fert, get_mort, pop_rebin, get_imm_resid, - immsolve, get_pop_objs, extrap_exp_3, extrap_arctan_3, - ab_zero_eqs_exp_func, b_zero_eq_arctan_func From 3214f65a8126e78d56fae5892844425bd87b6a86 Mon Sep 17 00:00:00 2001 From: jdebacker Date: Mon, 29 Jul 2024 23:23:56 -0400 Subject: [PATCH 05/30] updates to api docs --- docs/book/content/api/calibrate.rst | 2 +- docs/book/content/api/income.rst | 13 +++++++++++++ docs/book/content/api/input_output.rst | 12 ++++++++++++ docs/book/content/api/public_api.rst | 4 +++- docs/book/content/api/utils.rst | 17 +++++++++++++++++ 5 files changed, 46 insertions(+), 2 deletions(-) create mode 100644 docs/book/content/api/income.rst create mode 100644 docs/book/content/api/input_output.rst create mode 100644 docs/book/content/api/utils.rst diff --git a/docs/book/content/api/calibrate.rst b/docs/book/content/api/calibrate.rst index d06daf0..b986dd0 100644 --- a/docs/book/content/api/calibrate.rst +++ b/docs/book/content/api/calibrate.rst @@ -11,4 +11,4 @@ ogzaf.calibrate .. currentmodule:: ogzaf.calibrate .. autoclass:: Calibration - :members: get_tax_function_parameters, read_tax_func_estimate, get_dict + :members: get_dict diff --git a/docs/book/content/api/income.rst b/docs/book/content/api/income.rst new file mode 100644 index 0000000..9778c01 --- /dev/null +++ b/docs/book/content/api/income.rst @@ -0,0 +1,13 @@ +.. _income: + +Life-cycle productivity profiles +==================================== + +**income.py modules** + +ogzaf.income +------------------------------------------ + +.. automodule:: ogzaf.income + :members: arctan_func, arctan_deriv_func, arc_error, + arctan_fit, get_e_interp, get_e_orig diff --git a/docs/book/content/api/input_output.rst b/docs/book/content/api/input_output.rst new file mode 100644 index 0000000..a249760 --- /dev/null +++ b/docs/book/content/api/input_output.rst @@ -0,0 +1,12 @@ +.. _input_output: + +Output-Consumption Bridge +==================================== + +**input_output.py modules** + +ogzaf.input_output +------------------------------------------ + +.. automodule:: ogzaf.input_output + :members: get_alpha_c, get_io_matrix diff --git a/docs/book/content/api/public_api.rst b/docs/book/content/api/public_api.rst index 546de01..09ce241 100644 --- a/docs/book/content/api/public_api.rst +++ b/docs/book/content/api/public_api.rst @@ -12,5 +12,7 @@ There is also a link to the source code for each documented member. :maxdepth: 1 calibrate - demographics + income + input_output macro_params + utils diff --git a/docs/book/content/api/utils.rst b/docs/book/content/api/utils.rst new file mode 100644 index 0000000..cf9a683 --- /dev/null +++ b/docs/book/content/api/utils.rst @@ -0,0 +1,17 @@ +.. _utils: + +Calibration Utilities +==================================== + +**utils.py modules** + +ogzaf.utils +------------------------------------------ + +.. currentmodule:: ogzaf.utils + +.. autoclass:: CustomHttpAdapter + :members: init_poolmanager + +.. automodule:: ogzaf.utils + :members: get_legacy_session From 7e01e1ceb7535495a302046cdead0d5cf78f136d Mon Sep 17 00:00:00 2001 From: jdebacker Date: Mon, 29 Jul 2024 23:39:13 -0400 Subject: [PATCH 06/30] sphinx setting to not print value of default arg --- docs/book/_config.yml | 1 + 1 file changed, 1 insertion(+) diff --git a/docs/book/_config.yml b/docs/book/_config.yml index bfa7b76..116c05b 100644 --- a/docs/book/_config.yml +++ b/docs/book/_config.yml @@ -52,6 +52,7 @@ sphinx: config : # key-value pairs to directly over-ride the Sphinx configuration bibtex_reference_style: author_year mathjax_path : https://cdn.jsdelivr.net/npm/mathjax@3/es5/tex-mml-chtml.js + autodoc_preserve_defaults: True #################################################### # LaTeX information From 581724d119ecb0fcde74459c00e5cd6422f6e75f Mon Sep 17 00:00:00 2001 From: jdebacker Date: Mon, 29 Jul 2024 23:39:53 -0400 Subject: [PATCH 07/30] remove unnecessary refs to USA earning profiles --- docs/book/content/calibration/earnings.md | 154 +--------------------- 1 file changed, 4 insertions(+), 150 deletions(-) diff --git a/docs/book/content/calibration/earnings.md b/docs/book/content/calibration/earnings.md index a499bdb..10c1ce1 100644 --- a/docs/book/content/calibration/earnings.md +++ b/docs/book/content/calibration/earnings.md @@ -14,9 +14,9 @@ kernelspec: (Chap_LfEarn)= # Lifetime Earnings Profiles -Among households in `OG-ZAF`, we model both age heterogeneity and within-age ability heterogeneity. We use this ability or productivity heterogeneity to generate the income heterogeneity that we see in the data. +Among households in `OG-ZAF`, we model variations in the labor productivity over the lifecycle and between households of different skill groups. Together, these variations in productivity generate a distribution of earnings that is calibrated to match the level of inequality in South Africa. This chapter describes the calibration of the lifecycle earnings profiles and the distribution of earnings in the model. -Differences among workers' productivity in terms of ability is one of the key dimensions of heterogeneity to model in a micro-founded macroeconomy. In this chapter, we characterize this heterogeneity as deterministic lifetime productivity paths to which new cohorts of agents in the model are randomly assigned. In `OG-ZAF`, households' labor income comes from the equilibrium wage and the agent's endogenous quantity of labor supply. In this section, we augment the labor income expression with an individual productivity $e_{j,s}$, where $j$ is the index of the ability type or path of the individual and $s$ is the age of the individual with that ability path. +Differences among workers' productivity is one of the key dimensions of heterogeneity to model in a micro-founded macroeconomy. In this chapter, we characterize this heterogeneity as deterministic lifetime productivity paths to which new cohorts of agents in the model are randomly assigned. In `OG-ZAF`, households' labor income comes from the equilibrium wage and the agent's endogenous quantity of labor supply. In this section, we augment the labor income expression with an individual productivity $e_{j,s}$, where $j$ is the index of the ability type or path of the individual and $s$ is the age of the individual with that ability path. ```{math} :label: EqLaborIncome @@ -25,11 +25,11 @@ Differences among workers' productivity in terms of ability is one of the key di In this specification, $w_t$ is an equilibrium wage representing a portion of labor income that is common to all workers. Individual quantity of labor supply is $n_{j,s,t}$, and $e_{j,s}$ represents a labor productivity factor that augments or diminishes the productivity of a worker's labor supply relative to average productivity. -We calibrate deterministic ability paths such that each lifetime income group has a different life-cycle profile of earnings. The distribution of income and wealth are often focal components of macroeconomic models. These calibrations require the use of microeconomic data on household incomes, but this level of data is not readily available for South Africa from public sources or surveys. To overcome this, we start with the proposition that estimated productivity curves calibrated for the [OG-USA](https://pslmodels.github.io/OG-USA/content/calibration/earnings.html) model, generated from micro-level earnings data, represent a generalized relationship between age and lifetime income {cite}`DeBackerEtAl:2017`. As such, our objective is to generate the curves for the U.S. and then adjust their generalized shapes to produce those for South Africa. In other words, our strategic approach is to apply South Africa's national distribution of income by age to the estimated U.S. data on income by age, and then use these re-distributed data to re-estimate the earning profile curves and use them for South Africa. This is done in two ways (in this order): +We calibrate deterministic productivity paths such that each lifetime income group has a different life-cycle profile of earnings. The distribution of income and wealth are often focal components of macroeconomic models. These calibrations require the use of microeconomic data on household incomes, but this level of data is not readily available for South Africa from public sources or surveys. To overcome this, we start with the proposition that estimated productivity curves calibrated for the [OG-USA](https://pslmodels.github.io/OG-USA/content/calibration/earnings.html) model, generated from micro-level earnings data, represent a generalized relationship between age and lifetime income {cite}`DeBackerEtAl:2017`. As such, our objective is to generate the curves for the U.S. and then adjust their generalized shapes to produce those for South Africa. In other words, our strategic approach is to begin with the lifecycle labor productivity profiles estimated from detailed U.S. data and then adjust these to match the distribution of income in South Africa. This is done in two ways (in this order): + 1. Adjustment by income ($J$): adjust the gaps between the U.S. curves to match South Africa's distribution between the $J$-income groups, using data from the World Inequality Database (WID); 2. Adjustment by age ($S$): adjust the shape of all the U.S. curves to match South Africa's national distribution of income per capita for each age year, using data from the National Transfer Accounts database (NTA). -The data for the U.S. come from the U.S. Internal Revenue Services's (IRS) Statistics of Income program (SOI) Continuous Work History Sample (CWHS). {cite}`DeBackerEtAl:2017` match the SOI data with Social Security Administration (SSA) data on age and Current Population Survey (CPS) data on hours in order to generate a non-top-coded measure of hourly wage. See {cite}`DeBackerRamnath:2017` for a detailed description of the methodology. ```{figure} ./images/ability_log_2D_ZAF.png --- @@ -63,150 +63,4 @@ Exogenous life cycle income ability paths $\log(e_{j,s})$ with $S=80$ and $J=7$ We calibrate the model such that each lifetime income group has a different life-cycle profile of earnings. Since the distribution on income and wealth are key aspects of our model, we calibrate these processes so that we can represent earners in the top 1 percent of the distribution of lifetime income. - -(SecLFearnLifInc)= -## Lifetime Income - - In our model, labor supply and savings, and thus lifetime income, are endogenous. We therefore define lifetime income as the present value of lifetime labor endowments and not the value of lifetime labor earnings. Note that our data are at the tax filing unit. We take this unit to be equivalent to a household. Because of differences in household structure (i.e., singles versus couples), our definition of lifetime labor income will be in per-adult terms. In particular, for filing units with a primary and secondary filer, our imputed wage represents the average hourly earnings between the two. When calculating lifetime income, we assign single- and couple-households the same labor endowment. This has the effect of making our lifetime income metric a per-adult metric; there is therefore not an over-representation of couple-households in the higher lifetime income groups simply because their time endowment is higher than for singles. We use the following approach to measure the lifetime income. - - First, since our panel data do not allow us to observe the complete life cycle of earnings for each household (because of sample attrition, death or the finite sample period of the data), we use an imputation to estimate wages in the years of the household's economic life for which they do not appear in the CWHS. To do this, we estimate the following equation, separately by household type (where household types are single male, single female, couple with male head, or couple with female head): - - ```{math} - :label: wage_step1 - ln(w_{i,t}) = \alpha_{i} + \beta_{1}age_{i,t} + \beta_{2}age_{i,t}^{2} + \beta_{3}*age_{i,t}^{3} + \varepsilon_{i,t} - ``` - - The parameter estimates, including the household fixed effects, from Equation {eq}`wage_step1` are shown in {numref}`TabWage_step1`. These estimates are then used to impute values for log wages in years of each households' economic life for which we do not have data. This creates a balanced panel of log wages of households with heads aged 21 to 80. The actual and imputed wage values are then used to calculate the net present value of lifetime labor endowments per adult for each household. Specifically, we define lifetime income for household $i$ as: - - ```{math} - :label: eqn:LI - LI_{i} = \sum_{t=21}^{80}\left(\frac{1}{1+r}\right)^{t-21}(w_{i,t}*4000) - ``` - - - ```{list-table} **Initial log wage regressions.** Source: CWHS data, 1991-2009. \*\* Significant at the 5-percent level ($p<0.05$). \*\*\* Significant at the 1-percent level ($p<0.01$). - :header-rows: 1 - :name: TabWage_step1 - * - Dependent variable - - Single male - - Single female - - Married male head - - Married female head - * - $Age$ - - 0.177\*\*\* - - 0.143\*\*\* - - 0.134\*\*\* - - 0.065\*\* - * - - - (0.006) - - (0.005) - - (0.004) - - (0.027) - * - $Age^2$ - - -0.003\*\*\* - - -0.002\*\*\* - - -0.002\*\*\* - - -0.000 - * - - - (0.000) - - (0.000) - - (0.000) - - (0.001) - * - $Age^3$ - - 0.000\*\*\* - - 0.000\*\*\* - - 0.000\*\*\* - - 0.000 - * - - - (0.000) - - (0.000) - - (0.000) - - (0.000) - * - $Constant$ - - -0.839\*\*\* - - -0.648\*\*\* - - -0.042 - - 1.004\*\*\* - * - - - (0.072) - - (0.070) - - (0.058) - - (0.376) - * - Adjusted $R^2$ - - -0.007 - - 0.011 - - -0.032 - - -0.324 - * - Observations - - 88,833 - - 96,670 - - 141,564 - - 6,314 - ``` - - - Note that households all have the same time endowment in each year (4000 hours). Thus, the amount of the time endowment scales the lifetime income up or down, but does not change the lifetime income of one household relative to another. This is not the case with the interest rate, $r$, which we fix at 4\%. Changes in the interest rate differentially impact the lifetime income calculation for different individuals because they may face different earnings profiles. For example, a higher interest rate would reduced the discounted present value of lifetime income for those individuals whose wage profiles peaked later in their economic life by a larger amount than it would reduce the discounted present value of lifetime income for individuals whose wage profiles peaked earlier. - - -## Profiles by Lifetime Income - - With observations of lifetime income for each household, we next sort households and find the percentile of the lifetime income distribution within which each household falls. With these percentiles, we create our lifetime income groupings. - ```{math} - :label: EqLfEarnLambda_j - \lambda_{j}=[0.25, 0.25, 0.2, 0.1, 0.1, 0.09, 0.01] - ``` - - That is, lifetime income group one includes those in below the 25th percentile, group two includes those from the 25th to the median, group three includes those from the median to the 70th percentile, group four includes those from the 70th to the 80th percentile, group 5 includes those from the 80th to 90th percentile, group 6 includes those from the 90th to 99th percentile, and group 7 consists of the top one percent in the lifetime income distribution. - - - To get a life-cycle profile of effective labor units for each group, we estimate the wage profile for each lifetime income group. We do this by estimating the following regression model Equation {eq}`` separately for each lifetime income group using data on actual (not imputed) wages: - - ```{math} - :label: EqWage_profile - ln(w_{j,t}) = \alpha_{j} + \beta_{1}age_{j,t} + \beta_{2}age_{j,t}^{2} + \beta_{3}*age_{j,t}^{3} + \varepsilon_{j,t} - ``` - - Life-cycle earnings profiles are implied by the parameters estimated from equation {eq}`EqWage_profile`, and are used to plot {numref}`Figure %s `. The arctan function used to extrapolate these estimates for model ages 80-100 takes the following form: - ```{math} - :label: EqLfEarnArctan - y = \left(\frac{-a}{\pi}\right)*arctan(bx+c)+\frac{a}{2} - ``` - where $x$ is age, and $a$, $b$, and $c$ are the parameters we search over for the best fit of the function to the following three criteria: 1) the value of the function should match the value of the data at age 80, 2) the slope of the arctan should match the slope of the data at age 80, and 3) the value of the function should match the value of the data at age 100 times a constant. This constant is 0.5 for all lifetime income groups, except for the second highest ability at 0.7 (otherwise, the second highest has a lower income than the third highest ability group in the last few years). - - -## Income at the very top - -In addition to lifecycle profiles of the seven percentile groups above, the model provides calibrations of income at the very top. This includes breaking out percentiles as fine as the top 0.01% of earners. The two alternative $\lambda$ vectors are $\lambda_{j}=[0.25, 0.25, 0.2, 0.1, 0.1, 0.09, 0.005, 0.004, 0.001]$ and $\lambda_{j}=[0.25, 0.25, 0.2, 0.1, 0.1, 0.09, 0.005, 0.004, 0.0009, 0.0001]$. - -Because we do not have U.S. panel data that allow us to observe such top percentile groups, we make the following assumptions in calibrating income at the very top. First, we assume the shape of the lifecycle profile of these top earners is the same as the top 1% overall. Second, we use 2018 estimates from the methodology of {cite}`PikettySaez:2003` to provide factors to scale the earnings process we estimate for groups inside the top 1%.[^PS_note] - - -```{list-table} **Incomes at the very top** Source: Piketty and Saez (2003) 2018 estimates. -:header-rows: 1 -:name: tab:top_incomes - -* - Income Percentile Range - - Ratio to Top 1% - - Mean Income -* - Top 1% - - $1,143,687 - - 1.000 -* - Top 1-0.5% - - $524,677 - - 0.459 -* - Top 0.5-0.1% - - $968,991 - - 0.847 -* - Top 0.1-0.01% - - $3,103,621 - - 2.714 -* - Top 0.1% - - $4,937,516 - - 4.317 -* - Top 0.01% - - $21,442,570 - - 18.749 -``` - - [^PS_note]: These data are available from the website of Emmanuel Saez: [https://eml.berkeley.edu/~saez/](https://eml.berkeley.edu/~saez/). We use numbers from Table0, Panel B, "Income excluding realized capital gains." From 5f1a396c67e2ba27624038aff6ab122f553f71ce Mon Sep 17 00:00:00 2001 From: jdebacker Date: Mon, 29 Jul 2024 23:40:42 -0400 Subject: [PATCH 08/30] move household preferences and firm production params to new chapters --- docs/book/content/calibration/firms.md | 14 +++++++ docs/book/content/calibration/households.md | 16 ++++++++ docs/book/content/calibration/macro.md | 41 +++------------------ 3 files changed, 36 insertions(+), 35 deletions(-) create mode 100644 docs/book/content/calibration/firms.md create mode 100644 docs/book/content/calibration/households.md diff --git a/docs/book/content/calibration/firms.md b/docs/book/content/calibration/firms.md new file mode 100644 index 0000000..61a23e4 --- /dev/null +++ b/docs/book/content/calibration/firms.md @@ -0,0 +1,14 @@ +(Chap_FirmCalib)= +# Calibration of Firms Parameters + +## Aggregate Production Function and Capital Accumulation + +The [OG-Core firm theory documentation](https://pslmodels.github.io/OG-Core/content/theory/firms.html) outlines the constant returns to scale, constant elasticity of substitution production function of the representative firm. This function has two parameters; the elasticity of substitution and capital's share of output. + +### Elasticity of substitution + +`OG-ZAF`'s default parameterization has an elasticity of substitution of $\varepsilon=1.0$, which implies a Cobb-Douglas production function. + +### Capital's share of output + +Here, we use a default value of $\gamma =0.40$. \ No newline at end of file diff --git a/docs/book/content/calibration/households.md b/docs/book/content/calibration/households.md new file mode 100644 index 0000000..16d5d6c --- /dev/null +++ b/docs/book/content/calibration/households.md @@ -0,0 +1,16 @@ +(Chap_HouseholdCalib)= +# Calibration of Household Preference Parameters + +## Behavioral Assumptions + +### Elasticity of labor supply + +As discussed in the [OG-Core household theory documentation](https://pslmodels.github.io/OG-Core/content/theory/households.html), we use the elliptical disutility of labor function developed by {cite}`EvansPhillips:2017`. We then fit the parameters of the elliptical utility function to match the marginal disutility from a constant Frisch elasticity function. `OG-ZAF` users enter the constant Frisch elasticity as a parameter. {cite}`Peterman:2016` finds a range of Frisch elasticities estimated from microeconomic and macroeconomic data. These range from 0 to 4. Peterman makes the case that in lifecycle models without an extensive margin for employment the Frisch elasticity should be higher. For `OG-ZAF` we take a default value of 0.4 from {cite}`Altonji:1986`. + +### Intertemporal elasticity of substitution + +The default value for the intertemporal elasticity of substitution, $\sigma$, is taken from {cite}`ABMW:1999`. We set $\sigma=1.5$. + +### Rate of time preference + +We take our default value for the rate of time preference parameter, $\beta$ from {cite}`Carroll:2009`. We set the value to $\beta=0.96$ (on an annual basis). diff --git a/docs/book/content/calibration/macro.md b/docs/book/content/calibration/macro.md index 01e2239..64bb348 100644 --- a/docs/book/content/calibration/macro.md +++ b/docs/book/content/calibration/macro.md @@ -1,36 +1,9 @@ (Chap_MacroCalib)= # Calibration of Macroeconomic Parameters -## Behavioral Assumptions - -### Elasticity of labor supply - -As discussed in the [OG-Core household theory documentation](https://pslmodels.github.io/OG-Core/content/theory/households.html), we use the elliptical disutility of labor function developed by {cite}`EvansPhillips:2017`. We then fit the parameters of the elliptical utility function to match the marginal disutility from a constant Frisch elasticity function. `OG-ZAF` users enter the constant Frisch elasticity as a parameter. {cite}`Peterman:2016` finds a range of Frisch elasticities estimated from microeconomic and macroeconomic data. These range from 0 to 4. Peterman makes the case that in lifecycle models without an extensive margin for employment the Frisch elasticity should be higher. For `OG-ZAF` we take a default value of 0.4 from {cite}`Altonji:1986`. - -### Intertemporal elasticity of substitution - -The default value for the intertemporal elasticity of substitution, $\sigma$, is taken from {cite}`ABMW:1999`. We set $\sigma=1.5$. - -### Rate of time preference - -We take our default value for the rate of time preference parameter, $\beta$ from {cite}`Carroll:2009`. We set the value to $\beta=0.96$ (on an annual basis). - - ## Economic Assumptions -As the default rate of labor augmenting technological change, $g_y$, we use a value of 3%. The average annual growth rate in GDP per capita in South Africa between 1961 and 2021 is 0.7% per year. - -## Aggregate Production Function and Capital Accumulation - -The [OG-Core firm theory documentation](https://pslmodels.github.io/OG-Core/content/theory/firms.html) outlines the constant returns to scale, constant elasticity of substitution production function of the representative firm. This function has two parameters; the elasticity of substitution and capital's share of output. - -### Elasticity of substitution - -`OG-ZAF`'s default parameterization has an elasticity of substitution of $\varepsilon=1.0$, which implies a Cobb-Douglas production function. - -### Capital's share of output - -Here, we use a default value of $\gamma =0.61$. +As the default rate of labor augmenting technological change, $g_y$, we use a value of 0%. The average annual growth rate in GDP per capita in South Africa between 2007 and 2023 is 0.0% per year. ## Open Economy Parameters @@ -38,28 +11,26 @@ Here, we use a default value of $\gamma =0.61$. The path of foreign holding of domestic debt is endogenous, but the initial period stock of debt held by foreign investors is exogenous. We set this parameter, `initial_foreign_debt_ratio` to 0.26, consistent with data from the World Bank WDI. - ### Foreign purchases of newly issued debt -We set $\zeta_D = 0.26$. This is the average share of foreign purchases of newly issued government debt found from the World Bank WDI. +We set $\zeta_D = 0.23$. This is the average share of foreign purchases of newly issued government debt found from the World Bank WDI. ### Foreign holdings of excess capital -We set $\zeta_K = 0.9$. - +We set $\zeta_K = 0.9$. Note, this parameter is harder to pin down from the data as foreign purchases on "excess" capital demand is not typically directly measured or reported. A value of 0.9 implies a high degree of openness to international capital flows. ## Government Debt, Spending and Transfers ### Government Debt -The path of government debt is endogenous. But the initial value is exogenous. To avoid converting between model units and dollars, we calibrate the initial debt to GDP ratio, rather than the dollar value of the debt. This is the model parameter $\alpha_D$. We compute this from the ratio of publicly held debt outstanding to GDP. Based on 2019 values, this gives us a ratio of 0.78. +The path of government debt is endogenous. But the initial value is exogenous. To avoid converting between model units and dollars, we calibrate the initial debt to GDP ratio, rather than the dollar value of the debt. This is the model parameter $\alpha_D$. We compute this from the ratio of publicly held debt outstanding to GDP. Based on 2023 values, this gives us a ratio of 0.59. ### Aggregate transfers -Aggregate (non-Social Security) transfers to households are set as a share of GDP with the parameter $\alpha_T$. We exclude Social Security from transfers since it is modeled specifically. With this definition, the share of transfers to GDP in 2015 is 0.04 according to Federal Reserve Economic Data (FRED). +Aggregate (non-Social Security) transfers to households are set as a share of GDP with the parameter $\alpha_T$. We exclude Social Security from transfers since it is modeled specifically. With this definition, the share of transfers to GDP in 2015 is 0.04 according to [IMF data](https://data.imf.org/?sk=b052f0f0-c166-43b6-84fa-47cccae3e219&hide_uv=1). ### Government expenditures Government spending on goods and services are also set as a share of GDP with the parameter $\alpha_G$. We define government spending as:
    Government Spending = Total Outlays - Transfers - Net Interest on Debt - Social Security
    -With this definition, the share of government expenditure to GDP is 0.27 based on FRED. +With this definition, the share of government expenditure to GDP is 0.267 based on [data from the IMF](https://data.imf.org/?sk=b052f0f0-c166-43b6-84fa-47cccae3e219&hide_uv=1). From c5386aad7af36442b77a5b3924a2ae5b807be145 Mon Sep 17 00:00:00 2001 From: jdebacker Date: Mon, 29 Jul 2024 23:41:00 -0400 Subject: [PATCH 09/30] move household preferences and firm production params to new chapters --- ogzaf/macro_params.py | 106 +++++++++++++++++++++--------------------- 1 file changed, 53 insertions(+), 53 deletions(-) diff --git a/ogzaf/macro_params.py b/ogzaf/macro_params.py index b4958dc..5605364 100644 --- a/ogzaf/macro_params.py +++ b/ogzaf/macro_params.py @@ -44,43 +44,43 @@ def get_macro_params(): } # pull series of interest using pandas_datareader - wb_data_a = wb.download( - indicator=wb_a_variable_dict.values(), - country=country_iso, - start=start, - end=end, - ) - wb_data_a.rename( - columns=dict((y, x) for x, y in wb_a_variable_dict.items()), - inplace=True, - ) + # wb_data_a = wb.download( + # indicator=wb_a_variable_dict.values(), + # country=country_iso, + # start=start, + # end=end, + # ) + # wb_data_a.rename( + # columns=dict((y, x) for x, y in wb_a_variable_dict.items()), + # inplace=True, + # ) """ This retrieves quarterly data from the World Bank Quarterly Public Sector Debt database. The command extracts all available data even when start and end dates are specified. """ - wb_q_variable_dict = { - "Gross PSD USD - domestic creditors": "DP.DOD.DECD.CR.PS.CD", - "Gross PSD USD - external creditors": "DP.DOD.DECX.CR.PS.CD", - "Gross PSD Gen Gov - percentage of GDP": "DP.DOD.DECT.CR.GG.Z1", - } + # wb_q_variable_dict = { + # "Gross PSD USD - domestic creditors": "DP.DOD.DECD.CR.PS.CD", + # "Gross PSD USD - external creditors": "DP.DOD.DECX.CR.PS.CD", + # "Gross PSD Gen Gov - percentage of GDP": "DP.DOD.DECT.CR.GG.Z1", + # } # pull series of interest using pandas_datareader - wb_data_q = wb.download( - indicator=wb_q_variable_dict.values(), - country=country_iso, - start=start, - end=end, - ) - wb_data_q.rename( - columns=dict((y, x) for x, y in wb_q_variable_dict.items()), - inplace=True, - ) - - # Remove the hierarchical index (country and year) of wb_data_q and create a single row index using year - wb_data_q = wb_data_q.reset_index() - wb_data_q = wb_data_q.set_index("year") + # wb_data_q = wb.download( + # indicator=wb_q_variable_dict.values(), + # country=country_iso, + # start=start, + # end=end, + # ) + # wb_data_q.rename( + # columns=dict((y, x) for x, y in wb_q_variable_dict.items()), + # inplace=True, + # ) + + # # Remove the hierarchical index (country and year) of wb_data_q and create a single row index using year + # wb_data_q = wb_data_q.reset_index() + # wb_data_q = wb_data_q.set_index("year") """ This retrieves labour share data from the ILOSTAT Data API @@ -115,31 +115,31 @@ def get_macro_params(): # print(fred_data.loc(str(baseline_date))) # find initial_debt_ratio - macro_parameters["initial_debt_ratio"] = ( - pd.Series(wb_data_q["Gross PSD Gen Gov - percentage of GDP"]).loc[ - baseline_yearquarter - ] - ) / 100 + # macro_parameters["initial_debt_ratio"] = ( + # pd.Series(wb_data_q["Gross PSD Gen Gov - percentage of GDP"]).loc[ + # baseline_yearquarter + # ] + # ) / 100 # find initial_foreign_debt_ratio - macro_parameters["initial_foreign_debt_ratio"] = pd.Series( - wb_data_q["Gross PSD USD - external creditors"] - / ( - wb_data_q["Gross PSD USD - domestic creditors"] - + wb_data_q["Gross PSD USD - external creditors"] - ) - ).loc[baseline_yearquarter] + # macro_parameters["initial_foreign_debt_ratio"] = pd.Series( + # wb_data_q["Gross PSD USD - external creditors"] + # / ( + # wb_data_q["Gross PSD USD - domestic creditors"] + # + wb_data_q["Gross PSD USD - external creditors"] + # ) + # ).loc[baseline_yearquarter] # find zeta_D (Share of new debt issues from government that are purchased by foreigners) - macro_parameters["zeta_D"] = [ - pd.Series( - wb_data_q["Gross PSD USD - external creditors"] - / ( - wb_data_q["Gross PSD USD - domestic creditors"] - + wb_data_q["Gross PSD USD - external creditors"] - ) - ).loc[baseline_yearquarter] - ] + # macro_parameters["zeta_D"] = [ + # pd.Series( + # wb_data_q["Gross PSD USD - external creditors"] + # / ( + # wb_data_q["Gross PSD USD - domestic creditors"] + # + wb_data_q["Gross PSD USD - external creditors"] + # ) + # ).loc[baseline_yearquarter] + # ] # alpha_T, non-social security benefits as a fraction of GDP # source: https://data.imf.org/?sk=b052f0f0-c166-43b6-84fa-47cccae3e219&hide_uv=1 @@ -162,9 +162,9 @@ def get_macro_params(): ] # find g_y - macro_parameters["g_y_annual"] = ( - wb_data_a["GDP per capita (constant 2015 US$)"].pct_change(-1).mean() - ) + # macro_parameters["g_y_annual"] = ( + # wb_data_a["GDP per capita (constant 2015 US$)"].pct_change(-1).mean() + # ) """" We want to use the non linear relationship estimated by From 17ee55c69c3ecedf443cb08a603470f1b9d4498d Mon Sep 17 00:00:00 2001 From: jdebacker Date: Mon, 29 Jul 2024 23:41:15 -0400 Subject: [PATCH 10/30] update multi ind run script --- examples/run_og_zaf_multiple_industry.py | 12 ++++-------- 1 file changed, 4 insertions(+), 8 deletions(-) diff --git a/examples/run_og_zaf_multiple_industry.py b/examples/run_og_zaf_multiple_industry.py index a40199f..e769736 100644 --- a/examples/run_og_zaf_multiple_industry.py +++ b/examples/run_og_zaf_multiple_industry.py @@ -14,7 +14,7 @@ from ogcore import output_tables as ot from ogcore import output_plots as op from ogcore.execute import runner -from ogcore.utils import safe_read_pickle +from ogcore.utils import safe_read_pickle, param_dump_json def main(): @@ -54,16 +54,9 @@ def main(): p.M = 4 p.I = 5 c = Calibration(p) - updated_params = c.get_dict() - p.update_specifications(updated_params) updated_params_tax = { - "etr_params": [[[0.35]]], - "mtrx_params": [[[0.35]]], - "mtry_params": [[[0.35]]], - "cit_rate": [[0.28, 0.28, 0.28, 0.28]], # order of industries is primary, energy, tertiary, secondary ex energy "Z": [[0.5, 0.4, 1.7, 1.0]], - # Rick had good suggestion of using last successful run to find starting values and then keep churning. "epsilon": [1.0, 1.0, 1.0, 1.0], "gamma": [0.67, 0.50, 0.45, 0.53], "gamma_g": [0.0, 0.0, 0.0, 0.0], @@ -72,6 +65,9 @@ def main(): } p.update_specifications(updated_params_tax) + # dump params to json + param_dump_json(p, os.path.join(base_dir, "model_params.json")) + # Run model start_time = time.time() runner(p, time_path=True, client=client) From 929ce48a66889ddeef81d6193a0786233f130695 Mon Sep 17 00:00:00 2001 From: jdebacker Date: Mon, 29 Jul 2024 23:52:18 -0400 Subject: [PATCH 11/30] improve notes about lightly calibrated values --- .../calibration/exogenous_parameters.md | 18 +----------------- docs/book/content/calibration/matching_lwi.md | 2 +- 2 files changed, 2 insertions(+), 18 deletions(-) diff --git a/docs/book/content/calibration/exogenous_parameters.md b/docs/book/content/calibration/exogenous_parameters.md index a82a2a3..15e212d 100644 --- a/docs/book/content/calibration/exogenous_parameters.md +++ b/docs/book/content/calibration/exogenous_parameters.md @@ -14,9 +14,8 @@ kernelspec: (Chap_Exog)= # Exogenous Parameters - [TODO: This chapter needs heavy updating. Would be nice to do something similar to API chapter. But it is also nice to have references and descriptions as in the table below.] - In this chapter, list the exogenous inputs to the model, options, and where the values come from (weak calibration vs. strong calibration). Point to the respective chapters for some of the inputs. Mention the code in [`ogzaf_default_parameters.json`](https://github.com/EAPD-DRB/OG-ZAF/blob/master/ogzaf/ogzaf_default_parameters.json) in the OG-ZAF repository and in [`default_parameters.json`](https://github.com/PSLmodels/OG-Core/blob/master/ogcore/default_parameters.json) and [`parameters.py`](https://github.com/PSLmodels/OG-Core/blob/master/ogcore/parameters.py) in the OG-Core repository. + The JSON file [`ogzaf_default_parameters.json`](https://github.com/EAPD-DRB/OG-ZAF/blob/master/ogzaf/ogzaf_default_parameters.json) provides values for all the model parameters used as defaults for OG-ZAF. Below, we provide a table highlighting some of the parameters describing the scale of the model (number of periods, aged, productivity types) and some parameters of the solution method (dampening parameter for TPI). The table below provides a list of the exogenous parameters and their baseline calibration values. 67cAjZ9YOxcN`Bh z0~>K`TatQ%aR;2<-rf}>=|ztJ_bh-;6@~w=cSI(>_a3V%DT(->+CdMkqs~oDMb;%O zMy4|bgD8zfp9|VGJl1f%5=J?o{hfaOcNOx_1!O#EE!bIderIxm>PIT9m-n6UadPo+#9SNj8WS_9M$kU%5vdFw>^VrU>M!ByIK}k5lawnkp)c_$>!9z^)nNoohe)YIrBLfTRPldDf- zv0jca;zdEbhAs`<4*YQx#8=nX*MJLmt`+EJZkth9tQ@%NW+5(sK=9|Tn3(D_EZl>{ z@&_z3Im~;9k{N*zlVU}HaE0JJ#)_a*FbzCHMk8_aR+9iK=zV$m!g27G7jfJm2v8j@ zEhVU}_I43r;Wp@}z|8&cyDeG!%yaYfa zXg^a1Tn69}CUp?w%cicIeenF=(Tl5OD={!-*gZ8EAO;IIEHMZ?dT?8+A@1Okv_eJ& zfW(KM1&Rzb#_;ekc+J_Id~R#f0+mei``{H%e);jBn*mCo4QxfJcWMd>QGKfdL6@E| z-;j+X1-}~vWG6s_dXcPzRDY|q@VjeLj5i=!z~Tj!^vP-A&&E(Pv==fs<>Az{^z+CU z?l$R;)MP0l6ZEmNa`f>0d~en9EHrreKAaiz5qc1J~Ti}l!HSad4{&_HZ-WL~l z6Pl~9V7#GWX5MXFa5k*77=Q*lM!eAO_i@My#whfKZ+-T^-_5bcviX6m;u8_wO;KQ= zZO2nc&r}Rh5X&j>0*B<`Z2!LZAuv;5b+Gu4$;ru5?h8Jy93>IJSWOZ3KV>Q* zuAg5&3cNo041m~k2}7U_p;n%kUT+(Cz(a-nVW6Ws-Cs!e2%c8v2OdaDy7l_OBOH|; zVluFcfte^XXsW1SeWil 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