├── .conda ├── bld.bat ├── build.sh └── meta.yaml ├── .github ├── CODEOWNERS ├── ISSUE_TEMPLATE │ ├── bug_report.md │ ├── enhancement.md │ └── feature_request.md ├── pull_request_template.md └── workflows │ └── continuous-integration-workflow.yml ├── .gitignore ├── .pre-commit-config.yaml ├── .readthedocs.yaml ├── LICENSE ├── MANIFEST.in ├── README.rst ├── codecov.yml ├── development ├── documentation │ └── scalability │ │ ├── run_single_scalability_exercise.py │ │ └── scalability_setup.py └── testing │ ├── __init__.py │ ├── conftest.py │ ├── notifications.py │ ├── regression.py │ ├── test_cli.py │ └── testing_pull_request.py ├── docs ├── Makefile ├── _static │ ├── cscubs-2019 │ │ ├── paper.pdf │ │ ├── presentation.pdf │ │ └── sources.zip │ ├── css │ │ └── custom.css │ ├── funding │ │ └── Becker_Sebastian_Arbeitsprogramm.pdf │ ├── images │ │ ├── Logo_DIW_Berlin.svg │ │ ├── Logo_TRA1.png │ │ ├── OSE_logo_RGB.svg │ │ ├── UNI_Bonn_Logo_Standard_RZ_RGB.svg │ │ ├── algorithm_value_function_iteration.svg │ │ ├── book.svg │ │ ├── books.svg │ │ ├── coding.svg │ │ ├── computer.svg │ │ ├── dividers.svg │ │ ├── edit.svg │ │ ├── event_tree.svg │ │ ├── gears.svg │ │ ├── graduate-cap.svg │ │ ├── light-bulb.svg │ │ ├── note.svg │ │ ├── number-blocks.svg │ │ ├── peoples.svg │ │ ├── research.svg │ │ ├── respy-logo.svg │ │ ├── road.svg │ │ ├── study.svg │ │ └── timing_events.svg │ ├── respy.mplstyle │ └── thesis_proposals │ │ ├── Salience_and_human_capital.pdf │ │ └── Sparsity_and_human_capital.pdf ├── _templates │ ├── custom-about-us.html │ └── custom-intro.html ├── about_us.rst ├── conf.py ├── development │ ├── contributing_to_respy.rst │ ├── index.rst │ ├── releases.rst │ ├── roadmap.rst │ └── template_for_tutorials.rst ├── explanations │ ├── bib.rst │ ├── calibration.rst │ ├── computational_implementation.rst │ ├── economic_model.rst │ ├── glossary.rst │ ├── implementation_kw94.rst │ ├── index.rst │ ├── introduction.rst │ ├── mathematical_framework.rst │ ├── notation.rst │ ├── parameterization.rst │ ├── recommended_reading.rst │ └── refs.bib ├── how_to_guides │ ├── how_to_covariates.ipynb │ ├── how_to_example_models.rst │ ├── how_to_finite_mixture.ipynb │ ├── how_to_hyperbolic_discounting.ipynb │ ├── how_to_initial_conditions.ipynb │ ├── how_to_likelihood.ipynb │ ├── how_to_msm.ipynb │ ├── how_to_msm_estimation_exercise.ipynb │ ├── how_to_numerical_integration.ipynb │ ├── how_to_simulation.ipynb │ ├── how_to_specify_model.ipynb │ └── index.rst ├── index.rst ├── projects │ ├── _numerical_integration.py │ ├── estimating-keane-and-wolpin-1997-msm.ipynb │ ├── index.rst │ ├── keane-and-wolpin-1994.ipynb │ ├── numerical_integration.ipynb │ ├── research_papers.rst │ └── theses.rst ├── reference_guides │ ├── index.rst │ ├── randomness_and_reproducibility.rst │ ├── scalability.ipynb │ └── state_space.rst ├── release_notes.rst ├── rtd_environment.yml └── tutorials │ ├── index.rst │ ├── installation.rst │ ├── robinson_crusoe.ipynb │ ├── tutorial_exogenous_processes.ipynb │ ├── tutorial_experience.ipynb │ ├── tutorial_impatient_robinson.ipynb │ ├── tutorial_observables.ipynb │ └── tutorial_params_options_simulate.ipynb ├── environment.yml ├── respy ├── __init__.py ├── _numba.py ├── conditional_draws.py ├── config.py ├── conftest.py ├── data.py ├── exogenous_processes.py ├── interface.py ├── interpolate.py ├── likelihood.py ├── method_of_simulated_moments.py ├── parallelization.py ├── pre_processing │ ├── __init__.py │ ├── base_params.csv │ ├── data_checking.py │ ├── lagged_choice_params.csv │ ├── model_checking.py │ ├── model_processing.py │ ├── process_covariates.py │ └── specification_helpers.py ├── shared.py ├── simulate.py ├── solve.py ├── state_space.py └── tests │ ├── __init__.py │ ├── _former_code.py │ ├── random_model.py │ ├── resources │ ├── conditional_draws_fixture.pickle │ ├── kw_2000.csv │ ├── kw_2000.yaml │ ├── kw_2000_table_1_whites_choice_probabilities.csv │ ├── kw_2000_table_2_blacks_choice_probabilities.csv │ ├── kw_2000_table_3_wage_fit_blacks.csv │ ├── kw_2000_table_3_wage_fit_whites.csv │ ├── kw_2000_table_5_school_attainment.csv │ ├── kw_2000_table_a3_type_probabilities.csv │ ├── kw_94_one.csv │ ├── kw_94_one.yaml │ ├── kw_94_table_6.csv │ ├── kw_94_three.csv │ ├── kw_94_three.yaml │ ├── kw_94_two.csv │ ├── kw_94_two.yaml │ ├── kw_94_wp_table_2_1.csv │ ├── kw_94_wp_table_2_2.csv │ ├── kw_94_wp_table_2_3.csv │ ├── kw_97_basic.csv │ ├── kw_97_basic.yaml │ ├── kw_97_basic_respy.csv │ ├── kw_97_basic_respy.yaml │ ├── kw_97_data.csv │ ├── kw_97_extended.csv │ ├── kw_97_extended.yaml │ ├── kw_97_extended_respy.csv │ ├── kw_97_extended_respy.yaml │ ├── regression_vault.pickle │ ├── robinson_crusoe_basic.csv │ ├── robinson_crusoe_basic.yaml │ ├── robinson_crusoe_extended.csv │ ├── robinson_crusoe_extended.yaml │ ├── robinson_crusoe_with_observed_characteristics.csv │ └── robinson_crusoe_with_observed_characteristics.yaml │ ├── test_conditional_draws.py │ ├── test_exogenous_processes.py │ ├── test_flexible_choices.py │ ├── test_integration.py │ ├── test_interface.py │ ├── test_interpolate.py │ ├── test_likelihood.py │ ├── test_method_of_simulated_moments.py │ ├── test_model_processing.py │ ├── test_parallelization.py │ ├── test_process_covariates.py │ ├── test_randomness.py │ ├── test_regression.py │ ├── test_replication_kw_94.py │ ├── test_replication_kw_97.py │ ├── test_simulate.py │ ├── test_simulate │ ├── test_apply_law_of_motion_1_in.csv │ ├── test_apply_law_of_motion_1_optim_paras.yaml │ ├── test_apply_law_of_motion_1_out.csv │ ├── test_apply_law_of_motion_2_in.csv │ ├── test_apply_law_of_motion_2_optim_paras.yaml │ └── test_apply_law_of_motion_2_out.csv │ ├── test_solve.py │ └── utils.py ├── setup.cfg ├── setup.py └── tox.ini /.conda/bld.bat: -------------------------------------------------------------------------------- 1 | "%PYTHON%" setup.py install 2 | if errorlevel 1 exit 1 3 | 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