├── Aiyagari ├── Endogenous Grid Method │ ├── Version 1 │ │ ├── Aiyagari_EGM_Small.py │ │ ├── capital_supply_demand_aiyagari_small.pdf │ │ ├── consumption_policyfunction_egm_aiyagari_small.pdf │ │ ├── joint_density_egm_aiyagari_small_discrete.pdf │ │ ├── joint_density_egm_aiyagari_small_eigenvector.pdf │ │ ├── log10_euler_error_egm_aiyagari_small.pdf │ │ ├── requirements.txt │ │ ├── savings_policyfunction_egm_aiyagari_small.pdf │ │ ├── wealth_density_egm_aiyagari_small_discrete.pdf │ │ ├── wealth_density_egm_aiyagari_small_eigenvector.pdf │ │ └── wealth_density_egm_aiyagari_small_montecarlo.pdf │ └── Version 2 │ │ ├── Aiyagari_EGM.py │ │ ├── capital_supply_demand_aiyagari.pdf │ │ ├── consumption_policyfunction_egm_aiyagari.pdf │ │ ├── log10_euler_error_egm_aiyagari.pdf │ │ ├── requirements.txt │ │ ├── savings_policyfunction_egm_aiyagari.pdf │ │ ├── wealth_density_egm_aiyagari_discrete.pdf │ │ ├── wealth_density_egm_aiyagari_eigenvector.pdf │ │ └── wealth_density_egm_aiyagari_montecarlo.pdf ├── Policy Function Iteration │ ├── Version 1 │ │ ├── Aiyagari_PFI_Small.py │ │ ├── capital_supply_demand_aiyagari_small.pdf │ │ ├── consumption_policyfunction_pfi_aiyagari_small.pdf │ │ ├── joint_density_pfi_aiyagari_small_discrete.pdf │ │ ├── joint_density_pfi_aiyagari_small_eigenvector.pdf │ │ ├── log10_euler_error_pfi_aiyagari_small.pdf │ │ ├── requirements.txt │ │ ├── savings_policyfunction_pfi_aiyagari_small.pdf │ │ ├── wealth_density_pfi_aiyagari_small_discrete.pdf │ │ ├── wealth_density_pfi_aiyagari_small_eigenvector.pdf │ │ └── wealth_density_pfi_aiyagari_small_montecarlo.pdf │ └── Version 2 │ │ ├── Aiyagari_PFI.py │ │ ├── capital_supply_demand_aiyagari.pdf │ │ ├── consumption_policyfunction_pfi_aiyagari.pdf │ │ ├── log10_euler_error_pfi_aiyagari.pdf │ │ ├── requirements.txt │ │ ├── savings_policyfunction_pfi_aiyagari.pdf │ │ ├── wealth_density_pfi_aiyagari_discrete.pdf │ │ ├── wealth_density_pfi_aiyagari_eigenvector.pdf │ │ └── wealth_density_pfi_aiyagari_montecarlo.pdf ├── ReadMe.md └── Value Function Iteration │ ├── Version 1 │ ├── Aiyagari_VFI_Small.py │ ├── capital_supply_demand_aiyagari_small.pdf │ ├── consumption_policyfunction_vfi_aiyagari_small.pdf │ ├── joint_density_vfi_aiyagari_small_discrete.pdf │ ├── joint_density_vfi_aiyagari_small_eigenvector.pdf │ ├── log10_euler_error_vfi_aiyagari_small.pdf │ ├── requirements.txt │ ├── savings_policyfunction_vfi_aiyagari_small.pdf │ ├── value_function_vfi_aiyagari_small.pdf │ ├── wealth_density_vfi_aiyagari_small_discrete.pdf │ ├── wealth_density_vfi_aiyagari_small_eigenvector.pdf │ └── wealth_density_vfi_aiyagari_small_montecarlo.pdf │ └── Version 2 │ ├── Aiyagari_VFI.py │ ├── capital_supply_demand_aiyagari.pdf │ ├── consumption_policyfunction_vfi_aiyagari.pdf │ ├── log10_euler_error_vfi_aiyagari.pdf │ ├── requirements.txt │ ├── savings_policyfunction_vfi_aiyagari.pdf │ ├── value_function_vfi_aiyagari.pdf │ ├── wealth_density_vfi_aiyagari_discrete.pdf │ ├── wealth_density_vfi_aiyagari_eigenvector.pdf │ └── wealth_density_vfi_aiyagari_montecarlo.pdf ├── Consumption Saving ├── Endogenous Grid Method │ ├── Version 1 │ │ ├── ConSave_EGM_V1.py │ │ ├── consumption_policyfunction_egm_v1.pdf │ │ ├── joint_density_egm_v1_discrete.pdf │ │ ├── joint_density_egm_v1_eigenvector.pdf │ │ ├── log10_euler_error_egm_v1.pdf │ │ ├── requirements.txt │ │ ├── savings_policyfunction_egm_v1.pdf │ │ ├── simulation_egm_v1.pdf │ │ ├── wealth_density_egm_v1_discrete.pdf │ │ ├── wealth_density_egm_v1_eigenvector.pdf │ │ └── wealth_density_egm_v1_montecarlo.pdf │ └── Version 2 │ │ ├── ConSave_EGM_V2.py │ │ ├── consumption_policyfunction_egm_v2.pdf │ │ ├── log10_euler_error_egm_v2.pdf │ │ ├── requirements.txt │ │ ├── savings_policyfunction_egm_v2.pdf │ │ ├── simulation_egm_v2.pdf │ │ ├── wealth_density_egm_v2_discrete.pdf │ │ ├── wealth_density_egm_v2_eigenvector.pdf │ │ └── wealth_density_egm_v2_montecarlo.pdf ├── Policy Function Iteration │ ├── Version 1 │ │ ├── ConSave_PFI_V1.py │ │ ├── consumption_policyfunction_pfi_v1.pdf │ │ ├── joint_density_pfi_v1_discrete.pdf │ │ ├── joint_density_pfi_v1_eigenvector.pdf │ │ ├── log10_euler_error_pfi_v1.pdf │ │ ├── requirements.txt │ │ ├── savings_policyfunction_pfi_v1.pdf │ │ ├── simulation_pfi_v1.pdf │ │ ├── wealth_density_pfi_v1_discrete.pdf │ │ ├── wealth_density_pfi_v1_eigenvector.pdf │ │ └── wealth_density_pfi_v1_montecarlo.pdf │ └── Version 2 │ │ ├── ConSave_PFI_V2.py │ │ ├── consumption_policyfunction_pfi_v2.pdf │ │ ├── log10_euler_error_pfi_v2.pdf │ │ ├── requirements.txt │ │ ├── savings_policyfunction_pfi_v2.pdf │ │ ├── simulation_pfi_v2.pdf │ │ ├── wealth_density_pfi_v2_discrete.pdf │ │ ├── wealth_density_pfi_v2_eigenvector.pdf │ │ └── wealth_density_pfi_v2_montecarlo.pdf ├── ReadMe.md └── Value Function Iteration │ ├── Version 1 │ ├── ConSave_VFI_V1.py │ ├── consumption_policyfunction_vfi_v1.pdf │ ├── joint_density_vfi_v1_discrete.pdf │ ├── joint_density_vfi_v1_eigenvector.pdf │ ├── log10_euler_error_vfi_v1.pdf │ ├── requirements.txt │ ├── savings_policyfunction_vfi_v1.pdf │ ├── simulation_vfi_v1.pdf │ ├── value_function_vfi_v1.pdf │ ├── wealth_density_vfi_v1_discrete.pdf │ ├── wealth_density_vfi_v1_eigenvector.pdf │ └── wealth_density_vfi_v1_montecarlo.pdf │ └── Version 2 │ ├── ConSave_VFI_V2.py │ ├── consumption_policyfunction_vfi_v2.pdf │ ├── log10_euler_error_vfi_v2.pdf │ ├── requirements.txt │ ├── savings_policyfunction_vfi_v2.pdf │ ├── simulation_vfi_v2.pdf │ ├── value_function_vfi_v2.pdf │ ├── wealth_density_vfi_v2_discrete.pdf │ ├── wealth_density_vfi_v2_eigenvector.pdf │ └── wealth_density_vfi_v2_montecarlo.pdf ├── Hopenhayn ├── ReadMe.md ├── Version 1 │ ├── cdf_hopehayn.pdf │ ├── employment_by_firm_size_hopehayn.pdf │ ├── firm_size_hopehayn.pdf │ ├── hopenhayn_v1.py │ ├── pdf_hopehayn.pdf │ ├── requirements.txt │ └── value_func_hopehayn.pdf ├── Version 2 │ ├── cdf_hopehaynv2.pdf │ ├── employment_share_hopehaynv2.pdf │ ├── firm_size_hopehaynv2.pdf │ ├── hopenhayn_v2.py │ ├── pdf_hopehaynv2.pdf │ ├── requirements.txt │ └── vf_hopehaynv2.pdf └── Version 3 │ ├── hopenhayn_v3.py │ ├── pdf_hopehaynv3.pdf │ ├── pol_inv_hopehaynv3.pdf │ ├── pol_k_hopehaynv3.pdf │ ├── pol_n_hopehaynv3.pdf │ ├── requirements.txt │ └── vf_hopehaynv3.pdf ├── Neoclassical Growth ├── Matlab │ ├── markov.m │ ├── neoclassical_growth_deterministic.m │ ├── neoclassical_growth_stochastic.m │ ├── valfun_det.m │ └── valfun_stoch.m ├── Neoclassical Growth.md └── Python │ ├── Deterministic │ ├── VFI + Interpolation │ │ ├── Figures Solution │ │ │ ├── capital_policyfun_ncgm_vfi_interpolate.pdf │ │ │ ├── consumption_policyfun_ncgm_vfi_interpolate.pdf │ │ │ └── vf_ncgm_vfi_interpolate.pdf │ │ ├── Figures Transition │ │ │ ├── capital_transition_ncgm_vfi_interpolate.pdf │ │ │ ├── cheby_approx_ncgm_vfi_interpolate.pdf │ │ │ ├── consumption_transition_ncgm_vfi_interpolate.pdf │ │ │ ├── investment_transition_ncgm_vfi_interpolate.pdf │ │ │ └── output_transition_ncgm_vfi_interpolate.pdf │ │ ├── NCGM_Det_VFI_Interpolation.py │ │ └── requirements.txt │ └── VFI │ │ ├── Figures Solution │ │ ├── capital_policyfun_ncgm_vfi.pdf │ │ ├── consumption_policyfun_ncgm_vfi.pdf │ │ └── vf_ncgm_vfi.pdf │ │ ├── Figures Transition │ │ ├── capital_transition_ncgm_vfi.pdf │ │ ├── cheby_approx_ncgm_vfi.pdf │ │ ├── consumption_transition_ncgm_vfi.pdf │ │ ├── investment_transition_ncgm_vfi.pdf │ │ └── output_transition_ncgm_vfi.pdf │ │ ├── NCGM_Det_VFI.py │ │ └── requirements.txt │ ├── ReadMe.md │ └── Stochastic │ ├── VFI + Interpolation │ ├── Figures Simulation │ │ ├── capital_sim_ncgm_vfi_interpolate.pdf │ │ ├── consumption_sim_ncgm_vfi_interpolate.pdf │ │ ├── investment_sim_ncgm_vfi_interpolate.pdf │ │ └── output_sim_ncgm_vfi_interpolate.pdf │ ├── Figures Solution │ │ ├── capital_policyfun_ncgm_vfi_interpolate.pdf │ │ ├── consumption_policyfun_ncgm_vfi_interpolate.pdf │ │ ├── log10_euler_error_ncgm_vfi_interpolate.pdf │ │ └── vf_ncgm_vfi_interpolate.pdf │ ├── NCGM_Stoch_VFI_Interpolation.py │ └── requirements.txt │ └── VFI │ ├── Figures Simulation │ ├── capital_sim_ncgm_vfi.pdf │ ├── consumption_sim_ncgm_vfi.pdf │ ├── investment_sim_ncgm_vfi.pdf │ └── output_sim_ncgm_vfi.pdf │ ├── Figures Solution │ ├── capital_policyfun_ncgm_vfi.pdf │ ├── consumption_policyfun_ncgm_vfi.pdf │ ├── log10_euler_error_ncgm_vfi.pdf │ └── vf_ncgm_vfi.pdf │ ├── NCGM_Stoch_VFI.py │ └── requirements.txt ├── RANK ├── DSGE │ ├── Standard │ │ ├── mediumscale.mod │ │ ├── mediumscale_ss.m │ │ └── run_mediumscale.m │ ├── ZLB │ │ ├── medium_scale_dsge_2016.pdf │ │ ├── mediumscale_baseline.mod │ │ ├── mediumscale_ss_zlb.m │ │ ├── mediumscale_zlb.mod │ │ └── run_mediumscale_zlb.m │ └── data_moments_bea.csv ├── New Keynesian │ ├── Standard │ │ ├── new_keynesian.mod │ │ ├── nk_steadystate.m │ │ ├── policy_shock_nk.eps │ │ ├── pref_shock_nk.eps │ │ ├── run_new_keynesian.m │ │ └── tech_shock_nk.eps │ └── ZLB │ │ ├── nk_baseline.mod │ │ ├── nk_ss.m │ │ ├── nk_zlb.mod │ │ ├── policy_shock_zlb.eps │ │ ├── pref_shock_zlb.eps │ │ ├── run_nk_zlb.m │ │ └── tech_shock_zlb.eps └── ReadMe.md ├── README.md └── Restuccia Rogerson ├── ReadMe.md ├── cdf_model_v_data_rr08.pdf ├── cdf_rr08.pdf ├── employment_by_firm_size_rr08.pdf ├── establishment_dist.txt ├── firm_size_rr08.pdf ├── restuccia_rogerson.py ├── table_3_rr08.png └── table_5_rr08.png /Aiyagari/Endogenous Grid Method/Version 1/capital_supply_demand_aiyagari_small.pdf: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/hessjacob/Quantitative-Macro-Models/f548ac496dc0d440a5ecb251e01b84e4c0580641/Aiyagari/Endogenous Grid Method/Version 1/capital_supply_demand_aiyagari_small.pdf -------------------------------------------------------------------------------- /Aiyagari/Endogenous Grid Method/Version 1/consumption_policyfunction_egm_aiyagari_small.pdf: 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aiohttp==3.9.3 9 | aioitertools==0.7.1 10 | aiosignal==1.2.0 11 | aiosqlite==0.18.0 12 | alabaster==0.7.12 13 | altair==4.2.2 14 | anaconda-anon-usage==0.4.3 15 | anaconda-client==1.12.2 16 | anaconda-cloud-auth==0.1.4 17 | anaconda-navigator==2.5.2 18 | anyio==4.2.0 19 | appdirs==1.4.4 20 | applaunchservices==0.3.0 21 | appnope==0.1.2 22 | appscript==1.1.2 23 | archspec==0.2.1 24 | argon2-cffi==21.3.0 25 | argon2-cffi-bindings==21.2.0 26 | arrow==1.2.3 27 | astor==0.8.1 28 | astroid==2.14.2 29 | astropy==5.3.4 30 | asttokens==2.0.5 31 | async-lru==2.0.4 32 | async-timeout==4.0.3 33 | atomicwrites==1.4.0 34 | attrs==23.1.0 35 | Automat==20.2.0 36 | autopep8==2.0.4 37 | Babel==2.11.0 38 | backcall==0.2.0 39 | backports.functools-lru-cache==1.6.4 40 | backports.tempfile==1.0 41 | backports.weakref==1.0.post1 42 | bcrypt==3.2.0 43 | beautifulsoup4==4.12.2 44 | binaryornot==0.4.4 45 | bitarray==2.5.1 46 | bkcharts==0.2 47 | black==23.11.0 48 | bleach==4.1.0 49 | blinker==1.6.2 50 | bokeh==3.3.4 51 | boltons==23.0.0 52 | boto3==1.29.1 53 | botocore==1.32.1 54 | Bottleneck==1.3.7 55 | Brotli==1.0.9 56 | brotlipy==0.7.0 57 | cachetools==4.2.2 58 | certifi==2024.2.2 59 | cffi==1.16.0 60 | chardet==4.0.0 61 | charset-normalizer==2.0.4 62 | click==8.1.7 63 | cloudpickle==2.2.1 64 | clyent==1.2.2 65 | colorama==0.4.6 66 | colorcet==3.0.1 67 | comm==0.1.2 68 | conda==24.1.2 69 | conda-build==24.1.2 70 | conda-content-trust==0.2.0 71 | conda_index==0.4.0 72 | conda-libmamba-solver==24.1.0 73 | conda-package-handling==2.2.0 74 | conda_package_streaming==0.9.0 75 | conda-repo-cli==1.0.75 76 | conda-token==0.4.0 77 | conda-verify==3.4.2 78 | constantly==23.10.4 79 | contourpy==1.2.0 80 | cookiecutter==2.5.0 81 | cryptography==42.0.2 82 | cssselect==1.2.0 83 | cycler==0.11.0 84 | Cython==0.29.35 85 | cytoolz==0.12.2 86 | daal4py==2023.1.1 87 | dask==2023.11.0 88 | datashader==0.16.0 89 | datashape==0.5.4 90 | debugpy==1.6.7 91 | decorator==5.1.1 92 | defusedxml==0.7.1 93 | diff-match-patch==20200713 94 | dill==0.3.7 95 | distributed==2023.11.0 96 | distro==1.8.0 97 | docstring-to-markdown==0.11 98 | docutils==0.18.1 99 | entrypoints==0.4 100 | et-xmlfile==1.1.0 101 | exceptiongroup==1.2.0 102 | executing==0.8.3 103 | fastjsonschema==2.16.2 104 | filelock==3.13.1 105 | FixedEffectModelPyHDFE==0.0.5 106 | flake8==6.1.0 107 | Flask==2.2.5 108 | fonttools==4.25.0 109 | formulaic==0.6.3 110 | frozenlist==1.4.0 111 | fsspec==2023.10.0 112 | future==0.18.3 113 | gensim==4.3.0 114 | gitdb==4.0.7 115 | GitPython==3.1.37 116 | glob2==0.7 117 | gmpy2==2.1.2 118 | greenlet==3.0.1 119 | h5py==3.9.0 120 | HeapDict==1.0.1 121 | holoviews==1.18.3 122 | huggingface-hub==0.17.3 123 | hvplot==0.9.2 124 | hyperlink==21.0.0 125 | idna==3.4 126 | imagecodecs==2023.1.23 127 | imageio==2.33.1 128 | imagesize==1.4.1 129 | imbalanced-learn==0.11.0 130 | importlib-metadata==7.0.1 131 | importlib-resources==6.1.1 132 | incremental==21.3.0 133 | inflection==0.5.1 134 | iniconfig==1.1.1 135 | intake==0.6.8 136 | interface-meta==1.3.0 137 | interpolation==2.2.4 138 | intervaltree==3.1.0 139 | ipykernel==6.28.0 140 | ipython==8.15.0 141 | ipython-genutils==0.2.0 142 | ipywidgets==8.0.4 143 | isort==5.9.3 144 | itemadapter==0.3.0 145 | itemloaders==1.0.4 146 | itsdangerous==2.0.1 147 | jaraco.classes==3.2.1 148 | jdcal==1.4.1 149 | jedi==0.18.1 150 | jellyfish==1.0.1 151 | Jinja2==3.1.3 152 | jinja2-time==0.2.0 153 | jmespath==1.0.1 154 | joblib==1.2.0 155 | json5==0.9.6 156 | jsonpatch==1.32 157 | jsonpointer==2.1 158 | jsonschema==4.19.2 159 | jsonschema-specifications==2023.7.1 160 | jupyter==1.0.0 161 | jupyter_client==8.6.0 162 | jupyter-console==6.6.3 163 | jupyter_core==5.5.0 164 | jupyter-events==0.8.0 165 | jupyter-lsp==2.2.0 166 | jupyter_server==2.10.0 167 | jupyter_server_fileid==0.9.0 168 | jupyter-server-mathjax==0.2.6 169 | jupyter_server_terminals==0.4.4 170 | jupyter_server_ydoc==0.8.0 171 | jupyter-ydoc==0.2.4 172 | jupyterlab==4.0.11 173 | jupyterlab_git==0.50.0 174 | jupyterlab-pygments==0.1.2 175 | jupyterlab_server==2.25.1 176 | jupyterlab-widgets==3.0.9 177 | keyring==23.13.1 178 | kiwisolver==1.4.4 179 | lazy_loader==0.3 180 | lazy-object-proxy==1.6.0 181 | lckr_jupyterlab_variableinspector==3.1.0 182 | libarchive-c==2.9 183 | libmambapy==1.5.6 184 | linearmodels==5.1 185 | linkify-it-py==2.0.0 186 | llvmlite==0.42.0 187 | lmdb==1.4.1 188 | locket==1.0.0 189 | lxml==4.9.3 190 | lz4==4.3.2 191 | Markdown==3.4.1 192 | markdown-it-py==2.2.0 193 | MarkupSafe==2.1.3 194 | matplotlib==3.8.0 195 | matplotlib-inline==0.1.6 196 | mccabe==0.7.0 197 | mdit-py-plugins==0.3.0 198 | mdurl==0.1.0 199 | menuinst==2.0.2 200 | mistune==2.0.4 201 | mkl-fft==1.3.1 202 | mkl-random==1.2.2 203 | mkl-service==2.4.0 204 | more-itertools==10.1.0 205 | mpmath==1.3.0 206 | msgpack==1.0.3 207 | multidict==6.0.4 208 | multipledispatch==0.6.0 209 | munkres==1.1.4 210 | mypy==1.8.0 211 | mypy-extensions==1.0.0 212 | navigator-updater==0.4.0 213 | nbclassic==1.0.0 214 | nbclient==0.8.0 215 | nbconvert==7.10.0 216 | nbdime==4.0.1 217 | nbformat==5.9.2 218 | nest-asyncio==1.6.0 219 | networkx==3.1 220 | nltk==3.8.1 221 | nose==1.3.7 222 | notebook==7.0.8 223 | notebook_shim==0.2.3 224 | numba==0.59.0 225 | numexpr==2.8.7 226 | numpy==1.26.3 227 | numpydoc==1.5.0 228 | olefile==0.46 229 | openpyxl==3.0.10 230 | overrides==7.4.0 231 | packaging==21.3 232 | pandas==2.1.4 233 | pandocfilters==1.5.0 234 | panel==1.3.8 235 | param==2.0.2 236 | parsel==1.8.1 237 | parso==0.8.3 238 | partd==1.4.1 239 | pathlib==1.0.1 240 | pathspec==0.10.3 241 | patsy==0.5.3 242 | pep8==1.7.1 243 | pexpect==4.8.0 244 | pickleshare==0.7.5 245 | pillow==10.2.0 246 | pip==23.3.1 247 | pkce==1.0.3 248 | pkginfo==1.9.6 249 | platformdirs==3.10.0 250 | plotly==5.9.0 251 | pluggy==1.0.0 252 | ply==3.11 253 | pooch==1.7.0 254 | poyo==0.5.0 255 | prometheus-client==0.14.1 256 | prompt-toolkit==3.0.43 257 | Protego==0.1.16 258 | protobuf==3.20.3 259 | psutil==5.9.0 260 | ptyprocess==0.7.0 261 | pure-eval==0.2.2 262 | py==1.11.0 263 | py-cpuinfo==9.0.0 264 | pyarrow==14.0.2 265 | pyasn1==0.4.8 266 | pyasn1-modules==0.2.8 267 | pycodestyle==2.11.1 268 | pycosat==0.6.6 269 | pycparser==2.21 270 | pyct==0.5.0 271 | pycurl==7.45.2 272 | pydantic==1.10.12 273 | pydeck==0.7.1 274 | PyDispatcher==2.0.5 275 | pydocstyle==6.3.0 276 | pyerfa==2.0.0 277 | pyflakes==3.1.0 278 | Pygments==2.15.1 279 | pyhdfe==0.1.2 280 | PyJWT==2.4.0 281 | pylint==2.16.2 282 | pylint-venv==3.0.3 283 | pyls-spyder==0.4.0 284 | Pympler==0.9 285 | pyobjc-core==9.0 286 | pyobjc-framework-Cocoa==9.0 287 | pyobjc-framework-CoreServices==9.0 288 | pyobjc-framework-FSEvents==9.0 289 | pyodbc==5.0.1 290 | pyOpenSSL==24.0.0 291 | pyparsing==3.0.9 292 | PyQt5==5.15.10 293 | PyQt5-sip==12.13.0 294 | PyQtWebEngine==5.15.6 295 | pyrsistent==0.20.0 296 | PySocks==1.7.1 297 | pytest==7.4.0 298 | python-dateutil==2.8.2 299 | python-dotenv==0.21.0 300 | python-json-logger==2.0.7 301 | python-lsp-black==1.2.1 302 | python-lsp-jsonrpc==1.1.2 303 | python-lsp-server==1.9.0 304 | python-slugify==5.0.2 305 | python-snappy==0.6.1 306 | pytoolconfig==1.2.6 307 | pytz==2023.3.post1 308 | pyviz_comms==3.0.0 309 | pywavelets==1.5.0 310 | PyYAML==6.0.1 311 | pyzmq==25.1.2 312 | QDarkStyle==3.2.3 313 | qstylizer==0.2.2 314 | QtAwesome==1.2.2 315 | qtconsole==5.5.0 316 | QtPy==2.4.1 317 | quantecon==0.7.0 318 | queuelib==1.6.2 319 | referencing==0.30.2 320 | regex==2023.10.3 321 | requests==2.31.0 322 | requests-file==1.5.1 323 | requests-toolbelt==1.0.0 324 | rfc3339-validator==0.1.4 325 | rfc3986-validator==0.1.1 326 | rich==13.3.5 327 | rope==1.7.0 328 | rpds-py==0.10.6 329 | Rtree==1.0.1 330 | ruamel.yaml==0.17.21 331 | ruamel.yaml.clib==0.2.6 332 | ruamel-yaml-conda==0.17.21 333 | s3fs==2023.10.0 334 | s3transfer==0.7.0 335 | scikit-image==0.22.0 336 | scikit-learn==1.3.0 337 | scikit-learn-intelex==20230426.61523 338 | scipy==1.11.4 339 | Scrapy==2.8.0 340 | seaborn==0.12.2 341 | semver==2.13.0 342 | Send2Trash==1.8.2 343 | service-identity==18.1.0 344 | setuptools==68.2.2 345 | setuptools-scm==7.1.0 346 | sip==6.7.12 347 | six==1.16.0 348 | smart-open==5.2.1 349 | smmap==4.0.0 350 | sniffio==1.3.0 351 | snowballstemmer==2.2.0 352 | sortedcollections==2.1.0 353 | sortedcontainers==2.4.0 354 | soupsieve==2.5 355 | Sphinx==5.0.2 356 | sphinxcontrib-applehelp==1.0.2 357 | sphinxcontrib-devhelp==1.0.2 358 | sphinxcontrib-htmlhelp==2.0.0 359 | sphinxcontrib-jsmath==1.0.1 360 | sphinxcontrib-qthelp==1.0.3 361 | sphinxcontrib-serializinghtml==1.1.5 362 | spyder==5.5.0 363 | spyder-kernels==2.5.0 364 | SQLAlchemy==2.0.25 365 | stack-data==0.2.0 366 | stata-setup==0.1.3 367 | statsmodels==0.14.0 368 | streamlit==1.22.0 369 | sympy==1.12 370 | tables==3.9.2 371 | tabulate==0.9.0 372 | TBB==0.2 373 | tblib==1.7.0 374 | Tempita==0.5.3.dev0 375 | tenacity==8.2.2 376 | terminado==0.17.1 377 | testpath==0.6.0 378 | text-unidecode==1.3 379 | textdistance==4.2.1 380 | threadpoolctl==2.2.0 381 | three-merge==0.1.1 382 | tifffile==2023.4.12 383 | tinycss2==1.2.1 384 | tldextract==3.2.0 385 | tokenizers==0.13.3 386 | toml==0.10.2 387 | tomli==2.0.1 388 | tomlkit==0.11.1 389 | toolz==0.12.0 390 | torch==1.13.1 391 | tornado==6.3.3 392 | tqdm==4.65.0 393 | traitlets==5.7.1 394 | transformers==4.24.0 395 | Twisted==22.10.0 396 | typing_extensions==4.9.0 397 | tzdata==2023.3 398 | tzlocal==2.1 399 | uc-micro-py==1.0.1 400 | ujson==5.4.0 401 | Unidecode==1.2.0 402 | urllib3==1.26.18 403 | validators==0.18.2 404 | w3lib==1.21.0 405 | watchdog==2.1.6 406 | wcwidth==0.2.5 407 | webencodings==0.5.1 408 | websocket-client==0.58.0 409 | Werkzeug==2.2.3 410 | whatthepatch==1.0.2 411 | wheel==0.41.2 412 | widgetsnbextension==4.0.5 413 | wrapt==1.14.1 414 | wurlitzer==3.0.2 415 | xarray==2023.6.0 416 | xlrd==2.0.1 417 | XlsxWriter==3.1.1 418 | xlwings==0.29.1 419 | xyzservices==2022.9.0 420 | y-py==0.5.9 421 | yapf==0.40.1 422 | yarl==1.9.3 423 | ypy-websocket==0.8.2 424 | zict==3.0.0 425 | zipp==3.17.0 426 | zope.interface==5.4.0 427 | zstandard==0.19.0 428 | -------------------------------------------------------------------------------- /Aiyagari/Endogenous Grid Method/Version 1/savings_policyfunction_egm_aiyagari_small.pdf: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/hessjacob/Quantitative-Macro-Models/f548ac496dc0d440a5ecb251e01b84e4c0580641/Aiyagari/Endogenous Grid Method/Version 1/savings_policyfunction_egm_aiyagari_small.pdf -------------------------------------------------------------------------------- /Aiyagari/Endogenous Grid Method/Version 1/wealth_density_egm_aiyagari_small_discrete.pdf: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/hessjacob/Quantitative-Macro-Models/f548ac496dc0d440a5ecb251e01b84e4c0580641/Aiyagari/Endogenous Grid Method/Version 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2/log10_euler_error_egm_aiyagari.pdf: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/hessjacob/Quantitative-Macro-Models/f548ac496dc0d440a5ecb251e01b84e4c0580641/Aiyagari/Endogenous Grid Method/Version 2/log10_euler_error_egm_aiyagari.pdf -------------------------------------------------------------------------------- /Aiyagari/Endogenous Grid Method/Version 2/requirements.txt: -------------------------------------------------------------------------------- 1 | 2 | #To install run 3 | # pip install -r /your path/requirements.txt 4 | 5 | 6 | aiobotocore==2.7.0 7 | aiofiles==22.1.0 8 | aiohttp==3.9.3 9 | aioitertools==0.7.1 10 | aiosignal==1.2.0 11 | aiosqlite==0.18.0 12 | alabaster==0.7.12 13 | altair==4.2.2 14 | anaconda-anon-usage==0.4.3 15 | anaconda-client==1.12.2 16 | anaconda-cloud-auth==0.1.4 17 | anaconda-navigator==2.5.2 18 | anyio==4.2.0 19 | appdirs==1.4.4 20 | applaunchservices==0.3.0 21 | appnope==0.1.2 22 | appscript==1.1.2 23 | archspec==0.2.1 24 | argon2-cffi==21.3.0 25 | argon2-cffi-bindings==21.2.0 26 | arrow==1.2.3 27 | astor==0.8.1 28 | astroid==2.14.2 29 | astropy==5.3.4 30 | asttokens==2.0.5 31 | async-lru==2.0.4 32 | async-timeout==4.0.3 33 | atomicwrites==1.4.0 34 | attrs==23.1.0 35 | Automat==20.2.0 36 | autopep8==2.0.4 37 | Babel==2.11.0 38 | backcall==0.2.0 39 | backports.functools-lru-cache==1.6.4 40 | backports.tempfile==1.0 41 | backports.weakref==1.0.post1 42 | bcrypt==3.2.0 43 | beautifulsoup4==4.12.2 44 | binaryornot==0.4.4 45 | bitarray==2.5.1 46 | bkcharts==0.2 47 | black==23.11.0 48 | bleach==4.1.0 49 | blinker==1.6.2 50 | bokeh==3.3.4 51 | boltons==23.0.0 52 | boto3==1.29.1 53 | botocore==1.32.1 54 | Bottleneck==1.3.7 55 | Brotli==1.0.9 56 | brotlipy==0.7.0 57 | cachetools==4.2.2 58 | certifi==2024.2.2 59 | cffi==1.16.0 60 | chardet==4.0.0 61 | charset-normalizer==2.0.4 62 | click==8.1.7 63 | cloudpickle==2.2.1 64 | clyent==1.2.2 65 | colorama==0.4.6 66 | colorcet==3.0.1 67 | comm==0.1.2 68 | conda==24.1.2 69 | conda-build==24.1.2 70 | conda-content-trust==0.2.0 71 | conda_index==0.4.0 72 | conda-libmamba-solver==24.1.0 73 | conda-package-handling==2.2.0 74 | conda_package_streaming==0.9.0 75 | conda-repo-cli==1.0.75 76 | conda-token==0.4.0 77 | conda-verify==3.4.2 78 | constantly==23.10.4 79 | contourpy==1.2.0 80 | cookiecutter==2.5.0 81 | cryptography==42.0.2 82 | cssselect==1.2.0 83 | cycler==0.11.0 84 | Cython==0.29.35 85 | cytoolz==0.12.2 86 | daal4py==2023.1.1 87 | dask==2023.11.0 88 | datashader==0.16.0 89 | datashape==0.5.4 90 | debugpy==1.6.7 91 | decorator==5.1.1 92 | defusedxml==0.7.1 93 | diff-match-patch==20200713 94 | dill==0.3.7 95 | distributed==2023.11.0 96 | distro==1.8.0 97 | docstring-to-markdown==0.11 98 | docutils==0.18.1 99 | entrypoints==0.4 100 | et-xmlfile==1.1.0 101 | exceptiongroup==1.2.0 102 | executing==0.8.3 103 | fastjsonschema==2.16.2 104 | filelock==3.13.1 105 | FixedEffectModelPyHDFE==0.0.5 106 | flake8==6.1.0 107 | Flask==2.2.5 108 | fonttools==4.25.0 109 | formulaic==0.6.3 110 | frozenlist==1.4.0 111 | fsspec==2023.10.0 112 | future==0.18.3 113 | gensim==4.3.0 114 | gitdb==4.0.7 115 | GitPython==3.1.37 116 | glob2==0.7 117 | gmpy2==2.1.2 118 | greenlet==3.0.1 119 | h5py==3.9.0 120 | HeapDict==1.0.1 121 | holoviews==1.18.3 122 | huggingface-hub==0.17.3 123 | hvplot==0.9.2 124 | hyperlink==21.0.0 125 | idna==3.4 126 | imagecodecs==2023.1.23 127 | imageio==2.33.1 128 | imagesize==1.4.1 129 | imbalanced-learn==0.11.0 130 | importlib-metadata==7.0.1 131 | importlib-resources==6.1.1 132 | incremental==21.3.0 133 | inflection==0.5.1 134 | iniconfig==1.1.1 135 | intake==0.6.8 136 | interface-meta==1.3.0 137 | interpolation==2.2.4 138 | intervaltree==3.1.0 139 | ipykernel==6.28.0 140 | ipython==8.15.0 141 | ipython-genutils==0.2.0 142 | ipywidgets==8.0.4 143 | isort==5.9.3 144 | itemadapter==0.3.0 145 | itemloaders==1.0.4 146 | itsdangerous==2.0.1 147 | jaraco.classes==3.2.1 148 | jdcal==1.4.1 149 | jedi==0.18.1 150 | jellyfish==1.0.1 151 | Jinja2==3.1.3 152 | jinja2-time==0.2.0 153 | jmespath==1.0.1 154 | joblib==1.2.0 155 | json5==0.9.6 156 | jsonpatch==1.32 157 | jsonpointer==2.1 158 | jsonschema==4.19.2 159 | jsonschema-specifications==2023.7.1 160 | jupyter==1.0.0 161 | jupyter_client==8.6.0 162 | jupyter-console==6.6.3 163 | jupyter_core==5.5.0 164 | jupyter-events==0.8.0 165 | jupyter-lsp==2.2.0 166 | jupyter_server==2.10.0 167 | jupyter_server_fileid==0.9.0 168 | jupyter-server-mathjax==0.2.6 169 | jupyter_server_terminals==0.4.4 170 | jupyter_server_ydoc==0.8.0 171 | jupyter-ydoc==0.2.4 172 | jupyterlab==4.0.11 173 | jupyterlab_git==0.50.0 174 | jupyterlab-pygments==0.1.2 175 | jupyterlab_server==2.25.1 176 | jupyterlab-widgets==3.0.9 177 | keyring==23.13.1 178 | kiwisolver==1.4.4 179 | lazy_loader==0.3 180 | lazy-object-proxy==1.6.0 181 | lckr_jupyterlab_variableinspector==3.1.0 182 | libarchive-c==2.9 183 | libmambapy==1.5.6 184 | linearmodels==5.1 185 | linkify-it-py==2.0.0 186 | llvmlite==0.42.0 187 | lmdb==1.4.1 188 | locket==1.0.0 189 | lxml==4.9.3 190 | lz4==4.3.2 191 | Markdown==3.4.1 192 | markdown-it-py==2.2.0 193 | MarkupSafe==2.1.3 194 | matplotlib==3.8.0 195 | matplotlib-inline==0.1.6 196 | mccabe==0.7.0 197 | mdit-py-plugins==0.3.0 198 | mdurl==0.1.0 199 | menuinst==2.0.2 200 | mistune==2.0.4 201 | mkl-fft==1.3.1 202 | mkl-random==1.2.2 203 | mkl-service==2.4.0 204 | more-itertools==10.1.0 205 | mpmath==1.3.0 206 | msgpack==1.0.3 207 | multidict==6.0.4 208 | multipledispatch==0.6.0 209 | munkres==1.1.4 210 | mypy==1.8.0 211 | mypy-extensions==1.0.0 212 | navigator-updater==0.4.0 213 | nbclassic==1.0.0 214 | nbclient==0.8.0 215 | nbconvert==7.10.0 216 | nbdime==4.0.1 217 | nbformat==5.9.2 218 | nest-asyncio==1.6.0 219 | networkx==3.1 220 | nltk==3.8.1 221 | nose==1.3.7 222 | notebook==7.0.8 223 | notebook_shim==0.2.3 224 | numba==0.59.0 225 | numexpr==2.8.7 226 | numpy==1.26.3 227 | numpydoc==1.5.0 228 | olefile==0.46 229 | openpyxl==3.0.10 230 | overrides==7.4.0 231 | packaging==21.3 232 | pandas==2.1.4 233 | pandocfilters==1.5.0 234 | panel==1.3.8 235 | param==2.0.2 236 | parsel==1.8.1 237 | parso==0.8.3 238 | partd==1.4.1 239 | pathlib==1.0.1 240 | pathspec==0.10.3 241 | patsy==0.5.3 242 | pep8==1.7.1 243 | pexpect==4.8.0 244 | pickleshare==0.7.5 245 | pillow==10.2.0 246 | pip==23.3.1 247 | pkce==1.0.3 248 | pkginfo==1.9.6 249 | platformdirs==3.10.0 250 | plotly==5.9.0 251 | pluggy==1.0.0 252 | ply==3.11 253 | pooch==1.7.0 254 | poyo==0.5.0 255 | prometheus-client==0.14.1 256 | prompt-toolkit==3.0.43 257 | Protego==0.1.16 258 | protobuf==3.20.3 259 | psutil==5.9.0 260 | ptyprocess==0.7.0 261 | pure-eval==0.2.2 262 | py==1.11.0 263 | py-cpuinfo==9.0.0 264 | pyarrow==14.0.2 265 | pyasn1==0.4.8 266 | pyasn1-modules==0.2.8 267 | pycodestyle==2.11.1 268 | pycosat==0.6.6 269 | pycparser==2.21 270 | pyct==0.5.0 271 | pycurl==7.45.2 272 | pydantic==1.10.12 273 | pydeck==0.7.1 274 | PyDispatcher==2.0.5 275 | pydocstyle==6.3.0 276 | pyerfa==2.0.0 277 | pyflakes==3.1.0 278 | Pygments==2.15.1 279 | pyhdfe==0.1.2 280 | PyJWT==2.4.0 281 | pylint==2.16.2 282 | pylint-venv==3.0.3 283 | pyls-spyder==0.4.0 284 | Pympler==0.9 285 | pyobjc-core==9.0 286 | pyobjc-framework-Cocoa==9.0 287 | pyobjc-framework-CoreServices==9.0 288 | pyobjc-framework-FSEvents==9.0 289 | pyodbc==5.0.1 290 | pyOpenSSL==24.0.0 291 | pyparsing==3.0.9 292 | PyQt5==5.15.10 293 | PyQt5-sip==12.13.0 294 | PyQtWebEngine==5.15.6 295 | pyrsistent==0.20.0 296 | PySocks==1.7.1 297 | pytest==7.4.0 298 | python-dateutil==2.8.2 299 | python-dotenv==0.21.0 300 | python-json-logger==2.0.7 301 | python-lsp-black==1.2.1 302 | python-lsp-jsonrpc==1.1.2 303 | python-lsp-server==1.9.0 304 | python-slugify==5.0.2 305 | python-snappy==0.6.1 306 | pytoolconfig==1.2.6 307 | pytz==2023.3.post1 308 | pyviz_comms==3.0.0 309 | pywavelets==1.5.0 310 | PyYAML==6.0.1 311 | pyzmq==25.1.2 312 | QDarkStyle==3.2.3 313 | qstylizer==0.2.2 314 | QtAwesome==1.2.2 315 | qtconsole==5.5.0 316 | QtPy==2.4.1 317 | quantecon==0.7.0 318 | queuelib==1.6.2 319 | referencing==0.30.2 320 | regex==2023.10.3 321 | requests==2.31.0 322 | requests-file==1.5.1 323 | requests-toolbelt==1.0.0 324 | rfc3339-validator==0.1.4 325 | rfc3986-validator==0.1.1 326 | rich==13.3.5 327 | rope==1.7.0 328 | rpds-py==0.10.6 329 | Rtree==1.0.1 330 | ruamel.yaml==0.17.21 331 | ruamel.yaml.clib==0.2.6 332 | ruamel-yaml-conda==0.17.21 333 | s3fs==2023.10.0 334 | s3transfer==0.7.0 335 | scikit-image==0.22.0 336 | scikit-learn==1.3.0 337 | scikit-learn-intelex==20230426.61523 338 | scipy==1.11.4 339 | Scrapy==2.8.0 340 | seaborn==0.12.2 341 | semver==2.13.0 342 | Send2Trash==1.8.2 343 | service-identity==18.1.0 344 | setuptools==68.2.2 345 | setuptools-scm==7.1.0 346 | sip==6.7.12 347 | six==1.16.0 348 | smart-open==5.2.1 349 | smmap==4.0.0 350 | sniffio==1.3.0 351 | snowballstemmer==2.2.0 352 | sortedcollections==2.1.0 353 | sortedcontainers==2.4.0 354 | soupsieve==2.5 355 | Sphinx==5.0.2 356 | sphinxcontrib-applehelp==1.0.2 357 | sphinxcontrib-devhelp==1.0.2 358 | sphinxcontrib-htmlhelp==2.0.0 359 | sphinxcontrib-jsmath==1.0.1 360 | sphinxcontrib-qthelp==1.0.3 361 | sphinxcontrib-serializinghtml==1.1.5 362 | spyder==5.5.0 363 | spyder-kernels==2.5.0 364 | SQLAlchemy==2.0.25 365 | stack-data==0.2.0 366 | stata-setup==0.1.3 367 | statsmodels==0.14.0 368 | streamlit==1.22.0 369 | sympy==1.12 370 | tables==3.9.2 371 | tabulate==0.9.0 372 | TBB==0.2 373 | tblib==1.7.0 374 | Tempita==0.5.3.dev0 375 | tenacity==8.2.2 376 | terminado==0.17.1 377 | testpath==0.6.0 378 | text-unidecode==1.3 379 | textdistance==4.2.1 380 | threadpoolctl==2.2.0 381 | three-merge==0.1.1 382 | tifffile==2023.4.12 383 | tinycss2==1.2.1 384 | tldextract==3.2.0 385 | tokenizers==0.13.3 386 | toml==0.10.2 387 | tomli==2.0.1 388 | tomlkit==0.11.1 389 | toolz==0.12.0 390 | torch==1.13.1 391 | tornado==6.3.3 392 | tqdm==4.65.0 393 | traitlets==5.7.1 394 | transformers==4.24.0 395 | Twisted==22.10.0 396 | typing_extensions==4.9.0 397 | tzdata==2023.3 398 | tzlocal==2.1 399 | uc-micro-py==1.0.1 400 | ujson==5.4.0 401 | Unidecode==1.2.0 402 | urllib3==1.26.18 403 | validators==0.18.2 404 | w3lib==1.21.0 405 | watchdog==2.1.6 406 | wcwidth==0.2.5 407 | webencodings==0.5.1 408 | websocket-client==0.58.0 409 | Werkzeug==2.2.3 410 | whatthepatch==1.0.2 411 | wheel==0.41.2 412 | widgetsnbextension==4.0.5 413 | wrapt==1.14.1 414 | wurlitzer==3.0.2 415 | xarray==2023.6.0 416 | xlrd==2.0.1 417 | XlsxWriter==3.1.1 418 | xlwings==0.29.1 419 | xyzservices==2022.9.0 420 | y-py==0.5.9 421 | yapf==0.40.1 422 | yarl==1.9.3 423 | ypy-websocket==0.8.2 424 | zict==3.0.0 425 | zipp==3.17.0 426 | zope.interface==5.4.0 427 | zstandard==0.19.0 428 | -------------------------------------------------------------------------------- /Aiyagari/Endogenous Grid Method/Version 2/savings_policyfunction_egm_aiyagari.pdf: 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arrow==1.2.3 27 | astor==0.8.1 28 | astroid==2.14.2 29 | astropy==5.3.4 30 | asttokens==2.0.5 31 | async-lru==2.0.4 32 | async-timeout==4.0.3 33 | atomicwrites==1.4.0 34 | attrs==23.1.0 35 | Automat==20.2.0 36 | autopep8==2.0.4 37 | Babel==2.11.0 38 | backcall==0.2.0 39 | backports.functools-lru-cache==1.6.4 40 | backports.tempfile==1.0 41 | backports.weakref==1.0.post1 42 | bcrypt==3.2.0 43 | beautifulsoup4==4.12.2 44 | binaryornot==0.4.4 45 | bitarray==2.5.1 46 | bkcharts==0.2 47 | black==23.11.0 48 | bleach==4.1.0 49 | blinker==1.6.2 50 | bokeh==3.3.4 51 | boltons==23.0.0 52 | boto3==1.29.1 53 | botocore==1.32.1 54 | Bottleneck==1.3.7 55 | Brotli==1.0.9 56 | brotlipy==0.7.0 57 | cachetools==4.2.2 58 | certifi==2024.2.2 59 | cffi==1.16.0 60 | chardet==4.0.0 61 | charset-normalizer==2.0.4 62 | click==8.1.7 63 | cloudpickle==2.2.1 64 | clyent==1.2.2 65 | colorama==0.4.6 66 | colorcet==3.0.1 67 | comm==0.1.2 68 | conda==24.1.2 69 | conda-build==24.1.2 70 | conda-content-trust==0.2.0 71 | conda_index==0.4.0 72 | conda-libmamba-solver==24.1.0 73 | conda-package-handling==2.2.0 74 | conda_package_streaming==0.9.0 75 | conda-repo-cli==1.0.75 76 | conda-token==0.4.0 77 | conda-verify==3.4.2 78 | constantly==23.10.4 79 | contourpy==1.2.0 80 | cookiecutter==2.5.0 81 | cryptography==42.0.2 82 | cssselect==1.2.0 83 | cycler==0.11.0 84 | Cython==0.29.35 85 | cytoolz==0.12.2 86 | daal4py==2023.1.1 87 | dask==2023.11.0 88 | datashader==0.16.0 89 | datashape==0.5.4 90 | debugpy==1.6.7 91 | decorator==5.1.1 92 | defusedxml==0.7.1 93 | diff-match-patch==20200713 94 | dill==0.3.7 95 | distributed==2023.11.0 96 | distro==1.8.0 97 | docstring-to-markdown==0.11 98 | docutils==0.18.1 99 | entrypoints==0.4 100 | et-xmlfile==1.1.0 101 | exceptiongroup==1.2.0 102 | executing==0.8.3 103 | fastjsonschema==2.16.2 104 | filelock==3.13.1 105 | FixedEffectModelPyHDFE==0.0.5 106 | flake8==6.1.0 107 | Flask==2.2.5 108 | fonttools==4.25.0 109 | formulaic==0.6.3 110 | frozenlist==1.4.0 111 | fsspec==2023.10.0 112 | future==0.18.3 113 | gensim==4.3.0 114 | gitdb==4.0.7 115 | GitPython==3.1.37 116 | glob2==0.7 117 | gmpy2==2.1.2 118 | greenlet==3.0.1 119 | h5py==3.9.0 120 | HeapDict==1.0.1 121 | holoviews==1.18.3 122 | huggingface-hub==0.17.3 123 | hvplot==0.9.2 124 | hyperlink==21.0.0 125 | idna==3.4 126 | imagecodecs==2023.1.23 127 | imageio==2.33.1 128 | imagesize==1.4.1 129 | imbalanced-learn==0.11.0 130 | importlib-metadata==7.0.1 131 | importlib-resources==6.1.1 132 | incremental==21.3.0 133 | inflection==0.5.1 134 | iniconfig==1.1.1 135 | intake==0.6.8 136 | interface-meta==1.3.0 137 | interpolation==2.2.4 138 | intervaltree==3.1.0 139 | ipykernel==6.28.0 140 | ipython==8.15.0 141 | ipython-genutils==0.2.0 142 | ipywidgets==8.0.4 143 | isort==5.9.3 144 | itemadapter==0.3.0 145 | itemloaders==1.0.4 146 | itsdangerous==2.0.1 147 | jaraco.classes==3.2.1 148 | jdcal==1.4.1 149 | jedi==0.18.1 150 | jellyfish==1.0.1 151 | Jinja2==3.1.3 152 | jinja2-time==0.2.0 153 | jmespath==1.0.1 154 | joblib==1.2.0 155 | json5==0.9.6 156 | jsonpatch==1.32 157 | jsonpointer==2.1 158 | jsonschema==4.19.2 159 | jsonschema-specifications==2023.7.1 160 | jupyter==1.0.0 161 | jupyter_client==8.6.0 162 | jupyter-console==6.6.3 163 | jupyter_core==5.5.0 164 | jupyter-events==0.8.0 165 | jupyter-lsp==2.2.0 166 | jupyter_server==2.10.0 167 | jupyter_server_fileid==0.9.0 168 | jupyter-server-mathjax==0.2.6 169 | jupyter_server_terminals==0.4.4 170 | jupyter_server_ydoc==0.8.0 171 | jupyter-ydoc==0.2.4 172 | jupyterlab==4.0.11 173 | jupyterlab_git==0.50.0 174 | jupyterlab-pygments==0.1.2 175 | jupyterlab_server==2.25.1 176 | jupyterlab-widgets==3.0.9 177 | keyring==23.13.1 178 | kiwisolver==1.4.4 179 | lazy_loader==0.3 180 | lazy-object-proxy==1.6.0 181 | lckr_jupyterlab_variableinspector==3.1.0 182 | libarchive-c==2.9 183 | libmambapy==1.5.6 184 | linearmodels==5.1 185 | linkify-it-py==2.0.0 186 | llvmlite==0.42.0 187 | lmdb==1.4.1 188 | locket==1.0.0 189 | lxml==4.9.3 190 | lz4==4.3.2 191 | Markdown==3.4.1 192 | markdown-it-py==2.2.0 193 | MarkupSafe==2.1.3 194 | matplotlib==3.8.0 195 | matplotlib-inline==0.1.6 196 | mccabe==0.7.0 197 | mdit-py-plugins==0.3.0 198 | mdurl==0.1.0 199 | menuinst==2.0.2 200 | mistune==2.0.4 201 | mkl-fft==1.3.1 202 | mkl-random==1.2.2 203 | mkl-service==2.4.0 204 | more-itertools==10.1.0 205 | mpmath==1.3.0 206 | msgpack==1.0.3 207 | multidict==6.0.4 208 | multipledispatch==0.6.0 209 | munkres==1.1.4 210 | mypy==1.8.0 211 | mypy-extensions==1.0.0 212 | navigator-updater==0.4.0 213 | nbclassic==1.0.0 214 | nbclient==0.8.0 215 | nbconvert==7.10.0 216 | nbdime==4.0.1 217 | nbformat==5.9.2 218 | nest-asyncio==1.6.0 219 | networkx==3.1 220 | nltk==3.8.1 221 | nose==1.3.7 222 | notebook==7.0.8 223 | notebook_shim==0.2.3 224 | numba==0.59.0 225 | numexpr==2.8.7 226 | numpy==1.26.3 227 | numpydoc==1.5.0 228 | olefile==0.46 229 | openpyxl==3.0.10 230 | overrides==7.4.0 231 | packaging==21.3 232 | pandas==2.1.4 233 | pandocfilters==1.5.0 234 | panel==1.3.8 235 | param==2.0.2 236 | parsel==1.8.1 237 | parso==0.8.3 238 | partd==1.4.1 239 | pathlib==1.0.1 240 | pathspec==0.10.3 241 | patsy==0.5.3 242 | pep8==1.7.1 243 | pexpect==4.8.0 244 | pickleshare==0.7.5 245 | pillow==10.2.0 246 | pip==23.3.1 247 | pkce==1.0.3 248 | pkginfo==1.9.6 249 | platformdirs==3.10.0 250 | plotly==5.9.0 251 | pluggy==1.0.0 252 | ply==3.11 253 | pooch==1.7.0 254 | poyo==0.5.0 255 | prometheus-client==0.14.1 256 | prompt-toolkit==3.0.43 257 | Protego==0.1.16 258 | protobuf==3.20.3 259 | psutil==5.9.0 260 | ptyprocess==0.7.0 261 | pure-eval==0.2.2 262 | py==1.11.0 263 | py-cpuinfo==9.0.0 264 | pyarrow==14.0.2 265 | pyasn1==0.4.8 266 | pyasn1-modules==0.2.8 267 | pycodestyle==2.11.1 268 | pycosat==0.6.6 269 | pycparser==2.21 270 | pyct==0.5.0 271 | pycurl==7.45.2 272 | pydantic==1.10.12 273 | pydeck==0.7.1 274 | PyDispatcher==2.0.5 275 | pydocstyle==6.3.0 276 | pyerfa==2.0.0 277 | pyflakes==3.1.0 278 | Pygments==2.15.1 279 | pyhdfe==0.1.2 280 | PyJWT==2.4.0 281 | pylint==2.16.2 282 | pylint-venv==3.0.3 283 | pyls-spyder==0.4.0 284 | Pympler==0.9 285 | pyobjc-core==9.0 286 | pyobjc-framework-Cocoa==9.0 287 | pyobjc-framework-CoreServices==9.0 288 | pyobjc-framework-FSEvents==9.0 289 | pyodbc==5.0.1 290 | pyOpenSSL==24.0.0 291 | pyparsing==3.0.9 292 | PyQt5==5.15.10 293 | PyQt5-sip==12.13.0 294 | PyQtWebEngine==5.15.6 295 | pyrsistent==0.20.0 296 | PySocks==1.7.1 297 | pytest==7.4.0 298 | python-dateutil==2.8.2 299 | python-dotenv==0.21.0 300 | python-json-logger==2.0.7 301 | python-lsp-black==1.2.1 302 | python-lsp-jsonrpc==1.1.2 303 | python-lsp-server==1.9.0 304 | python-slugify==5.0.2 305 | python-snappy==0.6.1 306 | pytoolconfig==1.2.6 307 | pytz==2023.3.post1 308 | pyviz_comms==3.0.0 309 | pywavelets==1.5.0 310 | PyYAML==6.0.1 311 | pyzmq==25.1.2 312 | QDarkStyle==3.2.3 313 | qstylizer==0.2.2 314 | QtAwesome==1.2.2 315 | qtconsole==5.5.0 316 | QtPy==2.4.1 317 | quantecon==0.7.0 318 | queuelib==1.6.2 319 | referencing==0.30.2 320 | regex==2023.10.3 321 | requests==2.31.0 322 | requests-file==1.5.1 323 | requests-toolbelt==1.0.0 324 | rfc3339-validator==0.1.4 325 | rfc3986-validator==0.1.1 326 | rich==13.3.5 327 | rope==1.7.0 328 | rpds-py==0.10.6 329 | Rtree==1.0.1 330 | ruamel.yaml==0.17.21 331 | ruamel.yaml.clib==0.2.6 332 | ruamel-yaml-conda==0.17.21 333 | s3fs==2023.10.0 334 | s3transfer==0.7.0 335 | scikit-image==0.22.0 336 | scikit-learn==1.3.0 337 | scikit-learn-intelex==20230426.61523 338 | scipy==1.11.4 339 | Scrapy==2.8.0 340 | seaborn==0.12.2 341 | semver==2.13.0 342 | Send2Trash==1.8.2 343 | service-identity==18.1.0 344 | setuptools==68.2.2 345 | setuptools-scm==7.1.0 346 | sip==6.7.12 347 | six==1.16.0 348 | smart-open==5.2.1 349 | smmap==4.0.0 350 | sniffio==1.3.0 351 | snowballstemmer==2.2.0 352 | sortedcollections==2.1.0 353 | sortedcontainers==2.4.0 354 | soupsieve==2.5 355 | Sphinx==5.0.2 356 | sphinxcontrib-applehelp==1.0.2 357 | sphinxcontrib-devhelp==1.0.2 358 | sphinxcontrib-htmlhelp==2.0.0 359 | sphinxcontrib-jsmath==1.0.1 360 | sphinxcontrib-qthelp==1.0.3 361 | sphinxcontrib-serializinghtml==1.1.5 362 | spyder==5.5.0 363 | spyder-kernels==2.5.0 364 | SQLAlchemy==2.0.25 365 | stack-data==0.2.0 366 | stata-setup==0.1.3 367 | statsmodels==0.14.0 368 | streamlit==1.22.0 369 | sympy==1.12 370 | tables==3.9.2 371 | tabulate==0.9.0 372 | TBB==0.2 373 | tblib==1.7.0 374 | Tempita==0.5.3.dev0 375 | tenacity==8.2.2 376 | terminado==0.17.1 377 | testpath==0.6.0 378 | text-unidecode==1.3 379 | textdistance==4.2.1 380 | threadpoolctl==2.2.0 381 | three-merge==0.1.1 382 | tifffile==2023.4.12 383 | tinycss2==1.2.1 384 | tldextract==3.2.0 385 | tokenizers==0.13.3 386 | toml==0.10.2 387 | tomli==2.0.1 388 | tomlkit==0.11.1 389 | toolz==0.12.0 390 | torch==1.13.1 391 | tornado==6.3.3 392 | tqdm==4.65.0 393 | traitlets==5.7.1 394 | transformers==4.24.0 395 | Twisted==22.10.0 396 | typing_extensions==4.9.0 397 | tzdata==2023.3 398 | tzlocal==2.1 399 | uc-micro-py==1.0.1 400 | ujson==5.4.0 401 | Unidecode==1.2.0 402 | urllib3==1.26.18 403 | validators==0.18.2 404 | w3lib==1.21.0 405 | watchdog==2.1.6 406 | wcwidth==0.2.5 407 | webencodings==0.5.1 408 | websocket-client==0.58.0 409 | Werkzeug==2.2.3 410 | whatthepatch==1.0.2 411 | wheel==0.41.2 412 | widgetsnbextension==4.0.5 413 | wrapt==1.14.1 414 | wurlitzer==3.0.2 415 | xarray==2023.6.0 416 | xlrd==2.0.1 417 | XlsxWriter==3.1.1 418 | xlwings==0.29.1 419 | xyzservices==2022.9.0 420 | y-py==0.5.9 421 | yapf==0.40.1 422 | yarl==1.9.3 423 | ypy-websocket==0.8.2 424 | zict==3.0.0 425 | zipp==3.17.0 426 | zope.interface==5.4.0 427 | zstandard==0.19.0 428 | -------------------------------------------------------------------------------- /Aiyagari/Policy Function Iteration/Version 1/savings_policyfunction_pfi_aiyagari_small.pdf: -------------------------------------------------------------------------------- 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Heterogenous agents are infinitely lived and are exposed to idiosyncratic income risk. I solve the versions of the model with three different solution methods to solve the household problem. In addition, the user can choose from three different methods to calculate the stationary distribution. Version 2 can be used to replicate Aiyagari (1994). The labor supply is perfectly inelastic (exogenous). 4 | 5 | **Solution Methods** 6 | 7 | - Value Function Iteration with Discretization 8 | 9 | - Policy Function Iteration on Euler Equation with Linear Interpolation 10 | 11 | - Endogenous Grid Method 12 | 13 | **Versions** 14 | - Version 1 -- Two income states and a transition matrix both of which can be set by the user. 15 | - Version 2 -- Continuous income process which is discretely approximated up to seven different income states using the Rouwenhorst method. The Tauchen method is available in the code should the user want an exact replication. 16 | 17 | **Code Features** 18 | 19 | 1) The user can choose to find the stationary distribution with one of three methods: 20 | * Discrete approximation of the density function which conducts a fixed point iteration with linear interpolation 21 | * Eigenvector method to solve for the exact stationary density. 22 | * Monte carlo simulation with 50,000 households. 23 | 2) Exogenous borrowing constraint which the user can choose. 24 | 3) Calculation of the euler equation error both across the entire grid space and through a simulation. 25 | 4) Plots the capital supply and demand as functions of the capital stock. 26 | -------------------------------------------------------------------------------- /Aiyagari/Value Function Iteration/Version 1/capital_supply_demand_aiyagari_small.pdf: -------------------------------------------------------------------------------- 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arrow==1.2.3 27 | astor==0.8.1 28 | astroid==2.14.2 29 | astropy==5.3.4 30 | asttokens==2.0.5 31 | async-lru==2.0.4 32 | async-timeout==4.0.3 33 | atomicwrites==1.4.0 34 | attrs==23.1.0 35 | Automat==20.2.0 36 | autopep8==2.0.4 37 | Babel==2.11.0 38 | backcall==0.2.0 39 | backports.functools-lru-cache==1.6.4 40 | backports.tempfile==1.0 41 | backports.weakref==1.0.post1 42 | bcrypt==3.2.0 43 | beautifulsoup4==4.12.2 44 | binaryornot==0.4.4 45 | bitarray==2.5.1 46 | bkcharts==0.2 47 | black==23.11.0 48 | bleach==4.1.0 49 | blinker==1.6.2 50 | bokeh==3.3.4 51 | boltons==23.0.0 52 | boto3==1.29.1 53 | botocore==1.32.1 54 | Bottleneck==1.3.7 55 | Brotli==1.0.9 56 | brotlipy==0.7.0 57 | cachetools==4.2.2 58 | certifi==2024.2.2 59 | cffi==1.16.0 60 | chardet==4.0.0 61 | charset-normalizer==2.0.4 62 | click==8.1.7 63 | cloudpickle==2.2.1 64 | clyent==1.2.2 65 | colorama==0.4.6 66 | colorcet==3.0.1 67 | comm==0.1.2 68 | conda==24.1.2 69 | conda-build==24.1.2 70 | conda-content-trust==0.2.0 71 | conda_index==0.4.0 72 | conda-libmamba-solver==24.1.0 73 | conda-package-handling==2.2.0 74 | conda_package_streaming==0.9.0 75 | conda-repo-cli==1.0.75 76 | conda-token==0.4.0 77 | conda-verify==3.4.2 78 | constantly==23.10.4 79 | contourpy==1.2.0 80 | cookiecutter==2.5.0 81 | cryptography==42.0.2 82 | cssselect==1.2.0 83 | cycler==0.11.0 84 | Cython==0.29.35 85 | cytoolz==0.12.2 86 | daal4py==2023.1.1 87 | dask==2023.11.0 88 | datashader==0.16.0 89 | datashape==0.5.4 90 | debugpy==1.6.7 91 | decorator==5.1.1 92 | defusedxml==0.7.1 93 | diff-match-patch==20200713 94 | dill==0.3.7 95 | distributed==2023.11.0 96 | distro==1.8.0 97 | docstring-to-markdown==0.11 98 | docutils==0.18.1 99 | entrypoints==0.4 100 | et-xmlfile==1.1.0 101 | exceptiongroup==1.2.0 102 | executing==0.8.3 103 | fastjsonschema==2.16.2 104 | filelock==3.13.1 105 | FixedEffectModelPyHDFE==0.0.5 106 | flake8==6.1.0 107 | Flask==2.2.5 108 | fonttools==4.25.0 109 | formulaic==0.6.3 110 | frozenlist==1.4.0 111 | fsspec==2023.10.0 112 | future==0.18.3 113 | gensim==4.3.0 114 | gitdb==4.0.7 115 | GitPython==3.1.37 116 | glob2==0.7 117 | gmpy2==2.1.2 118 | greenlet==3.0.1 119 | h5py==3.9.0 120 | HeapDict==1.0.1 121 | holoviews==1.18.3 122 | huggingface-hub==0.17.3 123 | hvplot==0.9.2 124 | hyperlink==21.0.0 125 | idna==3.4 126 | imagecodecs==2023.1.23 127 | imageio==2.33.1 128 | imagesize==1.4.1 129 | imbalanced-learn==0.11.0 130 | importlib-metadata==7.0.1 131 | importlib-resources==6.1.1 132 | incremental==21.3.0 133 | inflection==0.5.1 134 | iniconfig==1.1.1 135 | intake==0.6.8 136 | interface-meta==1.3.0 137 | interpolation==2.2.4 138 | intervaltree==3.1.0 139 | ipykernel==6.28.0 140 | ipython==8.15.0 141 | ipython-genutils==0.2.0 142 | ipywidgets==8.0.4 143 | isort==5.9.3 144 | itemadapter==0.3.0 145 | itemloaders==1.0.4 146 | itsdangerous==2.0.1 147 | jaraco.classes==3.2.1 148 | jdcal==1.4.1 149 | jedi==0.18.1 150 | jellyfish==1.0.1 151 | Jinja2==3.1.3 152 | jinja2-time==0.2.0 153 | jmespath==1.0.1 154 | joblib==1.2.0 155 | json5==0.9.6 156 | jsonpatch==1.32 157 | jsonpointer==2.1 158 | jsonschema==4.19.2 159 | jsonschema-specifications==2023.7.1 160 | jupyter==1.0.0 161 | jupyter_client==8.6.0 162 | jupyter-console==6.6.3 163 | jupyter_core==5.5.0 164 | jupyter-events==0.8.0 165 | jupyter-lsp==2.2.0 166 | jupyter_server==2.10.0 167 | jupyter_server_fileid==0.9.0 168 | jupyter-server-mathjax==0.2.6 169 | jupyter_server_terminals==0.4.4 170 | jupyter_server_ydoc==0.8.0 171 | jupyter-ydoc==0.2.4 172 | jupyterlab==4.0.11 173 | jupyterlab_git==0.50.0 174 | jupyterlab-pygments==0.1.2 175 | jupyterlab_server==2.25.1 176 | jupyterlab-widgets==3.0.9 177 | keyring==23.13.1 178 | kiwisolver==1.4.4 179 | lazy_loader==0.3 180 | lazy-object-proxy==1.6.0 181 | lckr_jupyterlab_variableinspector==3.1.0 182 | libarchive-c==2.9 183 | libmambapy==1.5.6 184 | linearmodels==5.1 185 | linkify-it-py==2.0.0 186 | llvmlite==0.42.0 187 | lmdb==1.4.1 188 | locket==1.0.0 189 | lxml==4.9.3 190 | lz4==4.3.2 191 | Markdown==3.4.1 192 | markdown-it-py==2.2.0 193 | MarkupSafe==2.1.3 194 | matplotlib==3.8.0 195 | matplotlib-inline==0.1.6 196 | mccabe==0.7.0 197 | mdit-py-plugins==0.3.0 198 | mdurl==0.1.0 199 | menuinst==2.0.2 200 | mistune==2.0.4 201 | mkl-fft==1.3.1 202 | mkl-random==1.2.2 203 | mkl-service==2.4.0 204 | more-itertools==10.1.0 205 | mpmath==1.3.0 206 | msgpack==1.0.3 207 | multidict==6.0.4 208 | multipledispatch==0.6.0 209 | munkres==1.1.4 210 | mypy==1.8.0 211 | mypy-extensions==1.0.0 212 | navigator-updater==0.4.0 213 | nbclassic==1.0.0 214 | nbclient==0.8.0 215 | nbconvert==7.10.0 216 | nbdime==4.0.1 217 | nbformat==5.9.2 218 | nest-asyncio==1.6.0 219 | networkx==3.1 220 | nltk==3.8.1 221 | nose==1.3.7 222 | notebook==7.0.8 223 | notebook_shim==0.2.3 224 | numba==0.59.0 225 | numexpr==2.8.7 226 | numpy==1.26.3 227 | numpydoc==1.5.0 228 | olefile==0.46 229 | openpyxl==3.0.10 230 | overrides==7.4.0 231 | packaging==21.3 232 | pandas==2.1.4 233 | pandocfilters==1.5.0 234 | panel==1.3.8 235 | param==2.0.2 236 | parsel==1.8.1 237 | parso==0.8.3 238 | partd==1.4.1 239 | pathlib==1.0.1 240 | pathspec==0.10.3 241 | patsy==0.5.3 242 | pep8==1.7.1 243 | pexpect==4.8.0 244 | pickleshare==0.7.5 245 | pillow==10.2.0 246 | pip==23.3.1 247 | pkce==1.0.3 248 | pkginfo==1.9.6 249 | platformdirs==3.10.0 250 | plotly==5.9.0 251 | pluggy==1.0.0 252 | ply==3.11 253 | pooch==1.7.0 254 | poyo==0.5.0 255 | prometheus-client==0.14.1 256 | prompt-toolkit==3.0.43 257 | Protego==0.1.16 258 | protobuf==3.20.3 259 | psutil==5.9.0 260 | ptyprocess==0.7.0 261 | pure-eval==0.2.2 262 | py==1.11.0 263 | py-cpuinfo==9.0.0 264 | pyarrow==14.0.2 265 | pyasn1==0.4.8 266 | pyasn1-modules==0.2.8 267 | pycodestyle==2.11.1 268 | pycosat==0.6.6 269 | pycparser==2.21 270 | pyct==0.5.0 271 | pycurl==7.45.2 272 | pydantic==1.10.12 273 | pydeck==0.7.1 274 | PyDispatcher==2.0.5 275 | pydocstyle==6.3.0 276 | pyerfa==2.0.0 277 | pyflakes==3.1.0 278 | Pygments==2.15.1 279 | pyhdfe==0.1.2 280 | PyJWT==2.4.0 281 | pylint==2.16.2 282 | pylint-venv==3.0.3 283 | pyls-spyder==0.4.0 284 | Pympler==0.9 285 | pyobjc-core==9.0 286 | pyobjc-framework-Cocoa==9.0 287 | pyobjc-framework-CoreServices==9.0 288 | pyobjc-framework-FSEvents==9.0 289 | pyodbc==5.0.1 290 | pyOpenSSL==24.0.0 291 | pyparsing==3.0.9 292 | PyQt5==5.15.10 293 | PyQt5-sip==12.13.0 294 | PyQtWebEngine==5.15.6 295 | pyrsistent==0.20.0 296 | PySocks==1.7.1 297 | pytest==7.4.0 298 | python-dateutil==2.8.2 299 | python-dotenv==0.21.0 300 | python-json-logger==2.0.7 301 | python-lsp-black==1.2.1 302 | python-lsp-jsonrpc==1.1.2 303 | python-lsp-server==1.9.0 304 | python-slugify==5.0.2 305 | python-snappy==0.6.1 306 | pytoolconfig==1.2.6 307 | pytz==2023.3.post1 308 | pyviz_comms==3.0.0 309 | pywavelets==1.5.0 310 | PyYAML==6.0.1 311 | pyzmq==25.1.2 312 | QDarkStyle==3.2.3 313 | qstylizer==0.2.2 314 | QtAwesome==1.2.2 315 | qtconsole==5.5.0 316 | QtPy==2.4.1 317 | quantecon==0.7.0 318 | queuelib==1.6.2 319 | referencing==0.30.2 320 | regex==2023.10.3 321 | requests==2.31.0 322 | requests-file==1.5.1 323 | requests-toolbelt==1.0.0 324 | rfc3339-validator==0.1.4 325 | rfc3986-validator==0.1.1 326 | rich==13.3.5 327 | rope==1.7.0 328 | rpds-py==0.10.6 329 | Rtree==1.0.1 330 | ruamel.yaml==0.17.21 331 | ruamel.yaml.clib==0.2.6 332 | ruamel-yaml-conda==0.17.21 333 | s3fs==2023.10.0 334 | s3transfer==0.7.0 335 | scikit-image==0.22.0 336 | scikit-learn==1.3.0 337 | scikit-learn-intelex==20230426.61523 338 | scipy==1.11.4 339 | Scrapy==2.8.0 340 | seaborn==0.12.2 341 | semver==2.13.0 342 | Send2Trash==1.8.2 343 | service-identity==18.1.0 344 | setuptools==68.2.2 345 | setuptools-scm==7.1.0 346 | sip==6.7.12 347 | six==1.16.0 348 | smart-open==5.2.1 349 | smmap==4.0.0 350 | sniffio==1.3.0 351 | snowballstemmer==2.2.0 352 | sortedcollections==2.1.0 353 | sortedcontainers==2.4.0 354 | soupsieve==2.5 355 | Sphinx==5.0.2 356 | sphinxcontrib-applehelp==1.0.2 357 | sphinxcontrib-devhelp==1.0.2 358 | sphinxcontrib-htmlhelp==2.0.0 359 | sphinxcontrib-jsmath==1.0.1 360 | sphinxcontrib-qthelp==1.0.3 361 | sphinxcontrib-serializinghtml==1.1.5 362 | spyder==5.5.0 363 | spyder-kernels==2.5.0 364 | SQLAlchemy==2.0.25 365 | stack-data==0.2.0 366 | stata-setup==0.1.3 367 | statsmodels==0.14.0 368 | streamlit==1.22.0 369 | sympy==1.12 370 | tables==3.9.2 371 | tabulate==0.9.0 372 | TBB==0.2 373 | tblib==1.7.0 374 | Tempita==0.5.3.dev0 375 | tenacity==8.2.2 376 | terminado==0.17.1 377 | testpath==0.6.0 378 | text-unidecode==1.3 379 | textdistance==4.2.1 380 | threadpoolctl==2.2.0 381 | three-merge==0.1.1 382 | tifffile==2023.4.12 383 | tinycss2==1.2.1 384 | tldextract==3.2.0 385 | tokenizers==0.13.3 386 | toml==0.10.2 387 | tomli==2.0.1 388 | tomlkit==0.11.1 389 | toolz==0.12.0 390 | torch==1.13.1 391 | tornado==6.3.3 392 | tqdm==4.65.0 393 | traitlets==5.7.1 394 | transformers==4.24.0 395 | Twisted==22.10.0 396 | typing_extensions==4.9.0 397 | tzdata==2023.3 398 | tzlocal==2.1 399 | uc-micro-py==1.0.1 400 | ujson==5.4.0 401 | Unidecode==1.2.0 402 | urllib3==1.26.18 403 | validators==0.18.2 404 | w3lib==1.21.0 405 | watchdog==2.1.6 406 | wcwidth==0.2.5 407 | webencodings==0.5.1 408 | websocket-client==0.58.0 409 | Werkzeug==2.2.3 410 | whatthepatch==1.0.2 411 | wheel==0.41.2 412 | widgetsnbextension==4.0.5 413 | wrapt==1.14.1 414 | wurlitzer==3.0.2 415 | xarray==2023.6.0 416 | xlrd==2.0.1 417 | XlsxWriter==3.1.1 418 | xlwings==0.29.1 419 | xyzservices==2022.9.0 420 | y-py==0.5.9 421 | yapf==0.40.1 422 | yarl==1.9.3 423 | ypy-websocket==0.8.2 424 | zict==3.0.0 425 | zipp==3.17.0 426 | zope.interface==5.4.0 427 | zstandard==0.19.0 428 | -------------------------------------------------------------------------------- /Aiyagari/Value Function Iteration/Version 1/savings_policyfunction_vfi_aiyagari_small.pdf: -------------------------------------------------------------------------------- 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9 | aioitertools==0.7.1 10 | aiosignal==1.2.0 11 | aiosqlite==0.18.0 12 | alabaster==0.7.12 13 | altair==4.2.2 14 | anaconda-anon-usage==0.4.3 15 | anaconda-client==1.12.2 16 | anaconda-cloud-auth==0.1.4 17 | anaconda-navigator==2.5.2 18 | anyio==4.2.0 19 | appdirs==1.4.4 20 | applaunchservices==0.3.0 21 | appnope==0.1.2 22 | appscript==1.1.2 23 | archspec==0.2.1 24 | argon2-cffi==21.3.0 25 | argon2-cffi-bindings==21.2.0 26 | arrow==1.2.3 27 | astor==0.8.1 28 | astroid==2.14.2 29 | astropy==5.3.4 30 | asttokens==2.0.5 31 | async-lru==2.0.4 32 | async-timeout==4.0.3 33 | atomicwrites==1.4.0 34 | attrs==23.1.0 35 | Automat==20.2.0 36 | autopep8==2.0.4 37 | Babel==2.11.0 38 | backcall==0.2.0 39 | backports.functools-lru-cache==1.6.4 40 | backports.tempfile==1.0 41 | backports.weakref==1.0.post1 42 | bcrypt==3.2.0 43 | beautifulsoup4==4.12.2 44 | binaryornot==0.4.4 45 | bitarray==2.5.1 46 | bkcharts==0.2 47 | black==23.11.0 48 | bleach==4.1.0 49 | blinker==1.6.2 50 | bokeh==3.3.4 51 | boltons==23.0.0 52 | boto3==1.29.1 53 | botocore==1.32.1 54 | Bottleneck==1.3.7 55 | Brotli==1.0.9 56 | brotlipy==0.7.0 57 | cachetools==4.2.2 58 | certifi==2024.2.2 59 | cffi==1.16.0 60 | chardet==4.0.0 61 | charset-normalizer==2.0.4 62 | click==8.1.7 63 | cloudpickle==2.2.1 64 | clyent==1.2.2 65 | colorama==0.4.6 66 | colorcet==3.0.1 67 | comm==0.1.2 68 | conda==24.1.2 69 | conda-build==24.1.2 70 | conda-content-trust==0.2.0 71 | conda_index==0.4.0 72 | conda-libmamba-solver==24.1.0 73 | conda-package-handling==2.2.0 74 | conda_package_streaming==0.9.0 75 | conda-repo-cli==1.0.75 76 | conda-token==0.4.0 77 | conda-verify==3.4.2 78 | constantly==23.10.4 79 | contourpy==1.2.0 80 | cookiecutter==2.5.0 81 | cryptography==42.0.2 82 | cssselect==1.2.0 83 | cycler==0.11.0 84 | Cython==0.29.35 85 | cytoolz==0.12.2 86 | daal4py==2023.1.1 87 | dask==2023.11.0 88 | datashader==0.16.0 89 | datashape==0.5.4 90 | debugpy==1.6.7 91 | decorator==5.1.1 92 | defusedxml==0.7.1 93 | 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173 | jupyterlab_git==0.50.0 174 | jupyterlab-pygments==0.1.2 175 | jupyterlab_server==2.25.1 176 | jupyterlab-widgets==3.0.9 177 | keyring==23.13.1 178 | kiwisolver==1.4.4 179 | lazy_loader==0.3 180 | lazy-object-proxy==1.6.0 181 | lckr_jupyterlab_variableinspector==3.1.0 182 | libarchive-c==2.9 183 | libmambapy==1.5.6 184 | linearmodels==5.1 185 | linkify-it-py==2.0.0 186 | llvmlite==0.42.0 187 | lmdb==1.4.1 188 | locket==1.0.0 189 | lxml==4.9.3 190 | lz4==4.3.2 191 | Markdown==3.4.1 192 | markdown-it-py==2.2.0 193 | MarkupSafe==2.1.3 194 | matplotlib==3.8.0 195 | matplotlib-inline==0.1.6 196 | mccabe==0.7.0 197 | mdit-py-plugins==0.3.0 198 | mdurl==0.1.0 199 | menuinst==2.0.2 200 | mistune==2.0.4 201 | mkl-fft==1.3.1 202 | mkl-random==1.2.2 203 | mkl-service==2.4.0 204 | more-itertools==10.1.0 205 | mpmath==1.3.0 206 | msgpack==1.0.3 207 | multidict==6.0.4 208 | multipledispatch==0.6.0 209 | munkres==1.1.4 210 | mypy==1.8.0 211 | mypy-extensions==1.0.0 212 | navigator-updater==0.4.0 213 | nbclassic==1.0.0 214 | nbclient==0.8.0 215 | nbconvert==7.10.0 216 | nbdime==4.0.1 217 | nbformat==5.9.2 218 | nest-asyncio==1.6.0 219 | networkx==3.1 220 | nltk==3.8.1 221 | nose==1.3.7 222 | notebook==7.0.8 223 | notebook_shim==0.2.3 224 | numba==0.59.0 225 | numexpr==2.8.7 226 | numpy==1.26.3 227 | numpydoc==1.5.0 228 | olefile==0.46 229 | openpyxl==3.0.10 230 | overrides==7.4.0 231 | packaging==21.3 232 | pandas==2.1.4 233 | pandocfilters==1.5.0 234 | panel==1.3.8 235 | param==2.0.2 236 | parsel==1.8.1 237 | parso==0.8.3 238 | partd==1.4.1 239 | pathlib==1.0.1 240 | pathspec==0.10.3 241 | patsy==0.5.3 242 | pep8==1.7.1 243 | pexpect==4.8.0 244 | pickleshare==0.7.5 245 | pillow==10.2.0 246 | pip==23.3.1 247 | pkce==1.0.3 248 | pkginfo==1.9.6 249 | platformdirs==3.10.0 250 | plotly==5.9.0 251 | pluggy==1.0.0 252 | ply==3.11 253 | pooch==1.7.0 254 | poyo==0.5.0 255 | prometheus-client==0.14.1 256 | prompt-toolkit==3.0.43 257 | Protego==0.1.16 258 | protobuf==3.20.3 259 | psutil==5.9.0 260 | ptyprocess==0.7.0 261 | pure-eval==0.2.2 262 | py==1.11.0 263 | py-cpuinfo==9.0.0 264 | pyarrow==14.0.2 265 | pyasn1==0.4.8 266 | pyasn1-modules==0.2.8 267 | pycodestyle==2.11.1 268 | pycosat==0.6.6 269 | pycparser==2.21 270 | pyct==0.5.0 271 | pycurl==7.45.2 272 | pydantic==1.10.12 273 | pydeck==0.7.1 274 | PyDispatcher==2.0.5 275 | pydocstyle==6.3.0 276 | pyerfa==2.0.0 277 | pyflakes==3.1.0 278 | Pygments==2.15.1 279 | pyhdfe==0.1.2 280 | PyJWT==2.4.0 281 | pylint==2.16.2 282 | pylint-venv==3.0.3 283 | pyls-spyder==0.4.0 284 | Pympler==0.9 285 | pyobjc-core==9.0 286 | pyobjc-framework-Cocoa==9.0 287 | pyobjc-framework-CoreServices==9.0 288 | pyobjc-framework-FSEvents==9.0 289 | pyodbc==5.0.1 290 | pyOpenSSL==24.0.0 291 | pyparsing==3.0.9 292 | PyQt5==5.15.10 293 | PyQt5-sip==12.13.0 294 | PyQtWebEngine==5.15.6 295 | pyrsistent==0.20.0 296 | PySocks==1.7.1 297 | pytest==7.4.0 298 | python-dateutil==2.8.2 299 | python-dotenv==0.21.0 300 | python-json-logger==2.0.7 301 | python-lsp-black==1.2.1 302 | python-lsp-jsonrpc==1.1.2 303 | python-lsp-server==1.9.0 304 | python-slugify==5.0.2 305 | python-snappy==0.6.1 306 | pytoolconfig==1.2.6 307 | pytz==2023.3.post1 308 | pyviz_comms==3.0.0 309 | pywavelets==1.5.0 310 | PyYAML==6.0.1 311 | pyzmq==25.1.2 312 | QDarkStyle==3.2.3 313 | qstylizer==0.2.2 314 | QtAwesome==1.2.2 315 | qtconsole==5.5.0 316 | QtPy==2.4.1 317 | quantecon==0.7.0 318 | queuelib==1.6.2 319 | referencing==0.30.2 320 | regex==2023.10.3 321 | requests==2.31.0 322 | requests-file==1.5.1 323 | requests-toolbelt==1.0.0 324 | rfc3339-validator==0.1.4 325 | rfc3986-validator==0.1.1 326 | rich==13.3.5 327 | rope==1.7.0 328 | rpds-py==0.10.6 329 | Rtree==1.0.1 330 | ruamel.yaml==0.17.21 331 | ruamel.yaml.clib==0.2.6 332 | ruamel-yaml-conda==0.17.21 333 | s3fs==2023.10.0 334 | s3transfer==0.7.0 335 | scikit-image==0.22.0 336 | scikit-learn==1.3.0 337 | scikit-learn-intelex==20230426.61523 338 | scipy==1.11.4 339 | Scrapy==2.8.0 340 | 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for heterogenous agents that are infinitely lived in incomplete markets and are exposed idiosyncratic income risk. Each numbered version under different solution methods solves the same problem. Version 1 solves a small consumption savings problem with two income states. Version 2 approximates the income process with the Rouwenhorst method and has more income states. These codes are extended to solve for general equilibrium in the Aiyagari section. 3 | 4 | **Solution Methods** 5 | 6 | - Value Function Iteration with Discretization 7 | 8 | - Policy Function Iteration on Euler Equation with Linear Interpolation 9 | 10 | - Endogenous Grid Method 11 | 12 | **Versions** 13 | - Version 1 -- Two income states and a transition matrix both of which can be set by the user. 14 | - Version 2 -- Continuous income process which is discretely approximated up to seven different income states using the Rouwenhorst method. 15 | 16 | **Code Features** 17 | 18 | 1) The user can choose to find the stationary distribution with one of three methods: 19 | * Discrete approximation of the density function which conducts a fixed point iteration with linear interpolation 20 | * Eigenvector method to solve for the exact stationary density. 21 | * Monte carlo simulation with 50,000 households. 22 | 2) Exogenous borrowing constraint which the user can choose. 23 | 3) Calculation of the euler equation error both across the entire grid space and through a simulation. 24 | -------------------------------------------------------------------------------- /Consumption Saving/Value Function Iteration/Version 1/consumption_policyfunction_vfi_v1.pdf: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/hessjacob/Quantitative-Macro-Models/f548ac496dc0d440a5ecb251e01b84e4c0580641/Consumption Saving/Value Function Iteration/Version 1/consumption_policyfunction_vfi_v1.pdf -------------------------------------------------------------------------------- /Consumption Saving/Value Function Iteration/Version 1/joint_density_vfi_v1_discrete.pdf: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/hessjacob/Quantitative-Macro-Models/f548ac496dc0d440a5ecb251e01b84e4c0580641/Consumption Saving/Value Function Iteration/Version 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It is a partial equilibrium solution as the demand side of the economy is exogenously given and wages are normalized to one. 4 | 5 | *Detailed Description*. 6 | The economy consists of many firms that are competitive and produce a single homoegenous good. Every period incumbent firms choose whether to exit the market. There is free entry into the industry, subject to paying a fixed entry cost. The stationary equilibrium determines the price and quantity of the good and the amount of labor hired. The code is solved using value function iteration to solve the firm problem and analytically solves for the stationary distribution. 7 | 8 | This model lays the basis for heterogenous firm/industry dynamics models. Hopenhayn and Rogerson (1993) extend this to general equilibrium. In version 2 I extend this to general equilibrium by embedding the neoclassical growth model solving for the stationary equilibrium. 9 | 10 | **Version 2 -- Firm Dynamics (Hopenhayn 1992) and the Neoclassical Growth Model (household owns capital)** 11 | 12 | * The code embeds the standard neoclassical growth model into Hopenhayn (1992) and solves for the stationary equilibrium in which there is continuous 13 | entry/exit of firms. The model written here is a loose variant of Veracierto (2001) who was the first (to my knowledge) to write a neoclassical growth model with 14 | firm dynamics. I solve the household problem using value function iteration and approximate the stationary productivity distribution of firms by fixed point iteration. 15 | 16 | * This is a frictionless economy. 17 | 18 | *Detailed Description*. 19 | The difference between this model and Hopenhayn (1992) and its general equilibrium extension in Hopenhayn and Rogerson (1993) is: 20 | 21 | 1) Unlike in either model, there is a flexible form of capital that the firm is able to rent from households (therefore they make the investment decision) as is standard in the neoclassical growth model. 22 | 2) Labor is inelastically supplied by the household and not divisble like in Hopenhayn and Rogerson (1993). 23 | 24 | Agents are infinitely lived and ex-ante identical. There are complete markets which allows me to to construct a repersentative household. Firms, on the other hand, are heterogenous in their productivity.The economy considered is similar to the neoclassical growth model except for output, which is produced by a large number of establishments subject to idiosyncratic productivity shocks that induce them to expand and contract over time.There is no aggregate uncertainty.Establishments have access to a decreasing returns to scale technology, pay a one-time fixed cost of entry, and a fixed cost of operation every period. 25 | 26 | There is ongoing exogenous and endogenous entry/exit in the steady state. Firms may exogenously die with probability lambda every period. I include this so that there are large productive firms that might suddenly shut down. The timing is as follows. At the beginning of every period firms that receive an exit shock leave the market. Remaining firms draw their new productivity and endogenously decide whether to continue or shut down. Those that continue choose the capital and labor factor demands to maximize profits. Meanwhile, there is a mass of potential entrants who draw an intial productivity level and decide whether they should enter the market. Before entering they must pay a one-time fixed entry cost. 27 | 28 | **Version 3 -- Firm Dynamics (Hopenhayn 1992) and the Neoclassical Growth Model (firm owns capital)** 29 | 30 | * Firm dynamics model from Hopenhayn (1992) augmented to include capital accumulation and adjustment costs. Firms own their own capital stock and make investment decision subject to a convex and non-convex costs, making it different than version 2. The code solves for the stationary equilibrium with endogenous entry and exit. The model is in partial equilibrium as I do not define a goods market. The model is most similar to the stationary model in Clementi and Palazzo (2016). I solve the household problem using value function iteration and approximate the stationary joint distribution of firms by fixed point iteration on the law of motion. 31 | 32 | * The only friction in this economy is capital adjustment costs. The equilibrium allocations are second-best and efficient from the social planner's point of view. Absent of these frictions the equilibrium allocations are first-best. 33 | 34 | *Detailed Description*. This version differs from the version 2 model as follows: 35 | 1) The firm owns the capital stock and makes capital investment decisions. I allow for reversible investment (the firm can consume from its capital stock). 36 | 2) With non-convex adjustment costs depending on the states it might be optimal to not invest at all (inaction). 37 | 3) There are now two state variables (tfp and capital). 38 | 39 | # Neoclassical Growth Model (Deterministic and Stochastic) 40 | Social planner solution for the neoclassical growth model with inelastic labor supply (which coincides with the competitive equilibrium solution). Unless otherwise stated all solution methods are avaliable for both the deterministic and stochastic case. The versions differ in how the social planner problem is solved, but obtain the same solution. See the RBC files which is this model but with elastic labor supply. 41 | 42 | **Solution Methods** 43 | - Value Function Iteration with Discretization 44 | - Value Function Iteration with Linear Interpolation 45 | * Solved with conventional value function interation and interpolates with cubic splines to get the value for the value function between the grid points. The computation requires less iterations and is significantly faster than conventional value function iteration. 46 | 47 | **Code Features** 48 | 49 | 1) The deterministic files conduct a perfect foresight transition to the steady state. 50 | 2) The stochastic files generate a markov chain simulation and evaluate the accuracy of the solution by computing the euler equation errors. 51 | 3) The interpolation scheme (chosen by the user) for the transitions and simulations interpolates the decision rules with either cubic interpolation or by approximating them with chebyshev polynomials and OLS. 52 | -------------------------------------------------------------------------------- /Hopenhayn/Version 1/cdf_hopehayn.pdf: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/hessjacob/Quantitative-Macro-Models/f548ac496dc0d440a5ecb251e01b84e4c0580641/Hopenhayn/Version 1/cdf_hopehayn.pdf 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asttokens==2.0.5 31 | async-lru==2.0.4 32 | async-timeout==4.0.3 33 | atomicwrites==1.4.0 34 | attrs==23.1.0 35 | Automat==20.2.0 36 | autopep8==2.0.4 37 | Babel==2.11.0 38 | backcall==0.2.0 39 | backports.functools-lru-cache==1.6.4 40 | backports.tempfile==1.0 41 | backports.weakref==1.0.post1 42 | bcrypt==3.2.0 43 | beautifulsoup4==4.12.2 44 | binaryornot==0.4.4 45 | bitarray==2.5.1 46 | bkcharts==0.2 47 | black==23.11.0 48 | bleach==4.1.0 49 | blinker==1.6.2 50 | bokeh==3.3.4 51 | boltons==23.0.0 52 | boto3==1.29.1 53 | botocore==1.32.1 54 | Bottleneck==1.3.7 55 | Brotli==1.0.9 56 | brotlipy==0.7.0 57 | cachetools==4.2.2 58 | certifi==2024.2.2 59 | cffi==1.16.0 60 | chardet==4.0.0 61 | charset-normalizer==2.0.4 62 | click==8.1.7 63 | cloudpickle==2.2.1 64 | clyent==1.2.2 65 | colorama==0.4.6 66 | colorcet==3.0.1 67 | comm==0.1.2 68 | conda==24.1.2 69 | conda-build==24.1.2 70 | conda-content-trust==0.2.0 71 | conda_index==0.4.0 72 | conda-libmamba-solver==24.1.0 73 | conda-package-handling==2.2.0 74 | conda_package_streaming==0.9.0 75 | conda-repo-cli==1.0.75 76 | conda-token==0.4.0 77 | conda-verify==3.4.2 78 | constantly==23.10.4 79 | contourpy==1.2.0 80 | cookiecutter==2.5.0 81 | cryptography==42.0.2 82 | cssselect==1.2.0 83 | cycler==0.11.0 84 | Cython==0.29.35 85 | cytoolz==0.12.2 86 | daal4py==2023.1.1 87 | dask==2023.11.0 88 | datashader==0.16.0 89 | datashape==0.5.4 90 | debugpy==1.6.7 91 | decorator==5.1.1 92 | defusedxml==0.7.1 93 | diff-match-patch==20200713 94 | dill==0.3.7 95 | distributed==2023.11.0 96 | distro==1.8.0 97 | docstring-to-markdown==0.11 98 | docutils==0.18.1 99 | entrypoints==0.4 100 | et-xmlfile==1.1.0 101 | exceptiongroup==1.2.0 102 | executing==0.8.3 103 | fastjsonschema==2.16.2 104 | filelock==3.13.1 105 | FixedEffectModelPyHDFE==0.0.5 106 | flake8==6.1.0 107 | Flask==2.2.5 108 | fonttools==4.25.0 109 | formulaic==0.6.3 110 | frozenlist==1.4.0 111 | fsspec==2023.10.0 112 | future==0.18.3 113 | gensim==4.3.0 114 | gitdb==4.0.7 115 | GitPython==3.1.37 116 | glob2==0.7 117 | gmpy2==2.1.2 118 | greenlet==3.0.1 119 | h5py==3.9.0 120 | HeapDict==1.0.1 121 | holoviews==1.18.3 122 | huggingface-hub==0.17.3 123 | hvplot==0.9.2 124 | hyperlink==21.0.0 125 | idna==3.4 126 | imagecodecs==2023.1.23 127 | imageio==2.33.1 128 | imagesize==1.4.1 129 | imbalanced-learn==0.11.0 130 | importlib-metadata==7.0.1 131 | importlib-resources==6.1.1 132 | incremental==21.3.0 133 | inflection==0.5.1 134 | iniconfig==1.1.1 135 | intake==0.6.8 136 | interface-meta==1.3.0 137 | interpolation==2.2.4 138 | intervaltree==3.1.0 139 | ipykernel==6.28.0 140 | ipython==8.15.0 141 | ipython-genutils==0.2.0 142 | ipywidgets==8.0.4 143 | isort==5.9.3 144 | itemadapter==0.3.0 145 | itemloaders==1.0.4 146 | itsdangerous==2.0.1 147 | jaraco.classes==3.2.1 148 | jdcal==1.4.1 149 | jedi==0.18.1 150 | jellyfish==1.0.1 151 | Jinja2==3.1.3 152 | jinja2-time==0.2.0 153 | jmespath==1.0.1 154 | joblib==1.2.0 155 | json5==0.9.6 156 | jsonpatch==1.32 157 | jsonpointer==2.1 158 | jsonschema==4.19.2 159 | jsonschema-specifications==2023.7.1 160 | jupyter==1.0.0 161 | jupyter_client==8.6.0 162 | jupyter-console==6.6.3 163 | jupyter_core==5.5.0 164 | jupyter-events==0.8.0 165 | jupyter-lsp==2.2.0 166 | jupyter_server==2.10.0 167 | jupyter_server_fileid==0.9.0 168 | jupyter-server-mathjax==0.2.6 169 | jupyter_server_terminals==0.4.4 170 | jupyter_server_ydoc==0.8.0 171 | jupyter-ydoc==0.2.4 172 | jupyterlab==4.0.11 173 | jupyterlab_git==0.50.0 174 | jupyterlab-pygments==0.1.2 175 | jupyterlab_server==2.25.1 176 | jupyterlab-widgets==3.0.9 177 | keyring==23.13.1 178 | kiwisolver==1.4.4 179 | lazy_loader==0.3 180 | lazy-object-proxy==1.6.0 181 | lckr_jupyterlab_variableinspector==3.1.0 182 | libarchive-c==2.9 183 | libmambapy==1.5.6 184 | linearmodels==5.1 185 | linkify-it-py==2.0.0 186 | llvmlite==0.42.0 187 | lmdb==1.4.1 188 | locket==1.0.0 189 | lxml==4.9.3 190 | lz4==4.3.2 191 | Markdown==3.4.1 192 | markdown-it-py==2.2.0 193 | MarkupSafe==2.1.3 194 | matplotlib==3.8.0 195 | matplotlib-inline==0.1.6 196 | mccabe==0.7.0 197 | mdit-py-plugins==0.3.0 198 | mdurl==0.1.0 199 | menuinst==2.0.2 200 | mistune==2.0.4 201 | mkl-fft==1.3.1 202 | mkl-random==1.2.2 203 | mkl-service==2.4.0 204 | more-itertools==10.1.0 205 | mpmath==1.3.0 206 | msgpack==1.0.3 207 | multidict==6.0.4 208 | multipledispatch==0.6.0 209 | munkres==1.1.4 210 | mypy==1.8.0 211 | mypy-extensions==1.0.0 212 | navigator-updater==0.4.0 213 | nbclassic==1.0.0 214 | nbclient==0.8.0 215 | nbconvert==7.10.0 216 | nbdime==4.0.1 217 | nbformat==5.9.2 218 | nest-asyncio==1.6.0 219 | networkx==3.1 220 | nltk==3.8.1 221 | nose==1.3.7 222 | notebook==7.0.8 223 | notebook_shim==0.2.3 224 | numba==0.59.0 225 | numexpr==2.8.7 226 | numpy==1.26.3 227 | numpydoc==1.5.0 228 | olefile==0.46 229 | openpyxl==3.0.10 230 | overrides==7.4.0 231 | packaging==21.3 232 | pandas==2.1.4 233 | pandocfilters==1.5.0 234 | panel==1.3.8 235 | param==2.0.2 236 | parsel==1.8.1 237 | parso==0.8.3 238 | partd==1.4.1 239 | pathlib==1.0.1 240 | pathspec==0.10.3 241 | patsy==0.5.3 242 | pep8==1.7.1 243 | pexpect==4.8.0 244 | pickleshare==0.7.5 245 | pillow==10.2.0 246 | pip==23.3.1 247 | pkce==1.0.3 248 | pkginfo==1.9.6 249 | platformdirs==3.10.0 250 | plotly==5.9.0 251 | pluggy==1.0.0 252 | ply==3.11 253 | pooch==1.7.0 254 | poyo==0.5.0 255 | prometheus-client==0.14.1 256 | prompt-toolkit==3.0.43 257 | Protego==0.1.16 258 | protobuf==3.20.3 259 | psutil==5.9.0 260 | ptyprocess==0.7.0 261 | pure-eval==0.2.2 262 | py==1.11.0 263 | py-cpuinfo==9.0.0 264 | pyarrow==14.0.2 265 | pyasn1==0.4.8 266 | pyasn1-modules==0.2.8 267 | pycodestyle==2.11.1 268 | pycosat==0.6.6 269 | pycparser==2.21 270 | pyct==0.5.0 271 | pycurl==7.45.2 272 | pydantic==1.10.12 273 | pydeck==0.7.1 274 | PyDispatcher==2.0.5 275 | pydocstyle==6.3.0 276 | pyerfa==2.0.0 277 | pyflakes==3.1.0 278 | Pygments==2.15.1 279 | pyhdfe==0.1.2 280 | PyJWT==2.4.0 281 | pylint==2.16.2 282 | pylint-venv==3.0.3 283 | pyls-spyder==0.4.0 284 | Pympler==0.9 285 | pyobjc-core==9.0 286 | pyobjc-framework-Cocoa==9.0 287 | pyobjc-framework-CoreServices==9.0 288 | pyobjc-framework-FSEvents==9.0 289 | pyodbc==5.0.1 290 | pyOpenSSL==24.0.0 291 | pyparsing==3.0.9 292 | PyQt5==5.15.10 293 | PyQt5-sip==12.13.0 294 | PyQtWebEngine==5.15.6 295 | pyrsistent==0.20.0 296 | PySocks==1.7.1 297 | pytest==7.4.0 298 | python-dateutil==2.8.2 299 | python-dotenv==0.21.0 300 | python-json-logger==2.0.7 301 | python-lsp-black==1.2.1 302 | python-lsp-jsonrpc==1.1.2 303 | python-lsp-server==1.9.0 304 | python-slugify==5.0.2 305 | python-snappy==0.6.1 306 | pytoolconfig==1.2.6 307 | pytz==2023.3.post1 308 | pyviz_comms==3.0.0 309 | pywavelets==1.5.0 310 | PyYAML==6.0.1 311 | pyzmq==25.1.2 312 | QDarkStyle==3.2.3 313 | qstylizer==0.2.2 314 | QtAwesome==1.2.2 315 | qtconsole==5.5.0 316 | QtPy==2.4.1 317 | quantecon==0.7.0 318 | queuelib==1.6.2 319 | referencing==0.30.2 320 | regex==2023.10.3 321 | requests==2.31.0 322 | requests-file==1.5.1 323 | requests-toolbelt==1.0.0 324 | rfc3339-validator==0.1.4 325 | rfc3986-validator==0.1.1 326 | rich==13.3.5 327 | rope==1.7.0 328 | rpds-py==0.10.6 329 | Rtree==1.0.1 330 | ruamel.yaml==0.17.21 331 | ruamel.yaml.clib==0.2.6 332 | ruamel-yaml-conda==0.17.21 333 | s3fs==2023.10.0 334 | s3transfer==0.7.0 335 | scikit-image==0.22.0 336 | scikit-learn==1.3.0 337 | scikit-learn-intelex==20230426.61523 338 | scipy==1.11.4 339 | Scrapy==2.8.0 340 | seaborn==0.12.2 341 | semver==2.13.0 342 | Send2Trash==1.8.2 343 | service-identity==18.1.0 344 | setuptools==68.2.2 345 | setuptools-scm==7.1.0 346 | sip==6.7.12 347 | six==1.16.0 348 | smart-open==5.2.1 349 | smmap==4.0.0 350 | sniffio==1.3.0 351 | snowballstemmer==2.2.0 352 | sortedcollections==2.1.0 353 | sortedcontainers==2.4.0 354 | soupsieve==2.5 355 | Sphinx==5.0.2 356 | sphinxcontrib-applehelp==1.0.2 357 | sphinxcontrib-devhelp==1.0.2 358 | sphinxcontrib-htmlhelp==2.0.0 359 | sphinxcontrib-jsmath==1.0.1 360 | sphinxcontrib-qthelp==1.0.3 361 | sphinxcontrib-serializinghtml==1.1.5 362 | spyder==5.5.0 363 | spyder-kernels==2.5.0 364 | SQLAlchemy==2.0.25 365 | stack-data==0.2.0 366 | stata-setup==0.1.3 367 | statsmodels==0.14.0 368 | streamlit==1.22.0 369 | sympy==1.12 370 | tables==3.9.2 371 | tabulate==0.9.0 372 | TBB==0.2 373 | tblib==1.7.0 374 | Tempita==0.5.3.dev0 375 | tenacity==8.2.2 376 | terminado==0.17.1 377 | testpath==0.6.0 378 | text-unidecode==1.3 379 | textdistance==4.2.1 380 | threadpoolctl==2.2.0 381 | three-merge==0.1.1 382 | tifffile==2023.4.12 383 | tinycss2==1.2.1 384 | tldextract==3.2.0 385 | tokenizers==0.13.3 386 | toml==0.10.2 387 | tomli==2.0.1 388 | tomlkit==0.11.1 389 | toolz==0.12.0 390 | torch==1.13.1 391 | tornado==6.3.3 392 | tqdm==4.65.0 393 | traitlets==5.7.1 394 | transformers==4.24.0 395 | Twisted==22.10.0 396 | typing_extensions==4.9.0 397 | tzdata==2023.3 398 | tzlocal==2.1 399 | uc-micro-py==1.0.1 400 | ujson==5.4.0 401 | Unidecode==1.2.0 402 | urllib3==1.26.18 403 | validators==0.18.2 404 | w3lib==1.21.0 405 | watchdog==2.1.6 406 | wcwidth==0.2.5 407 | webencodings==0.5.1 408 | websocket-client==0.58.0 409 | Werkzeug==2.2.3 410 | whatthepatch==1.0.2 411 | wheel==0.41.2 412 | widgetsnbextension==4.0.5 413 | wrapt==1.14.1 414 | wurlitzer==3.0.2 415 | xarray==2023.6.0 416 | xlrd==2.0.1 417 | XlsxWriter==3.1.1 418 | xlwings==0.29.1 419 | xyzservices==2022.9.0 420 | y-py==0.5.9 421 | yapf==0.40.1 422 | yarl==1.9.3 423 | ypy-websocket==0.8.2 424 | zict==3.0.0 425 | zipp==3.17.0 426 | zope.interface==5.4.0 427 | zstandard==0.19.0 428 | -------------------------------------------------------------------------------- /Hopenhayn/Version 1/value_func_hopehayn.pdf: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/hessjacob/Quantitative-Macro-Models/f548ac496dc0d440a5ecb251e01b84e4c0580641/Hopenhayn/Version 1/value_func_hopehayn.pdf 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https://raw.githubusercontent.com/hessjacob/Quantitative-Macro-Models/f548ac496dc0d440a5ecb251e01b84e4c0580641/Hopenhayn/Version 2/firm_size_hopehaynv2.pdf -------------------------------------------------------------------------------- /Hopenhayn/Version 2/pdf_hopehaynv2.pdf: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/hessjacob/Quantitative-Macro-Models/f548ac496dc0d440a5ecb251e01b84e4c0580641/Hopenhayn/Version 2/pdf_hopehaynv2.pdf -------------------------------------------------------------------------------- /Hopenhayn/Version 2/requirements.txt: -------------------------------------------------------------------------------- 1 | 2 | #To install run 3 | # pip install -r /your path/requirements.txt 4 | 5 | 6 | aiobotocore==2.7.0 7 | aiofiles==22.1.0 8 | aiohttp==3.9.3 9 | aioitertools==0.7.1 10 | aiosignal==1.2.0 11 | aiosqlite==0.18.0 12 | alabaster==0.7.12 13 | altair==4.2.2 14 | anaconda-anon-usage==0.4.3 15 | anaconda-client==1.12.2 16 | anaconda-cloud-auth==0.1.4 17 | anaconda-navigator==2.5.2 18 | anyio==4.2.0 19 | appdirs==1.4.4 20 | applaunchservices==0.3.0 21 | appnope==0.1.2 22 | appscript==1.1.2 23 | archspec==0.2.1 24 | argon2-cffi==21.3.0 25 | argon2-cffi-bindings==21.2.0 26 | arrow==1.2.3 27 | astor==0.8.1 28 | astroid==2.14.2 29 | astropy==5.3.4 30 | asttokens==2.0.5 31 | async-lru==2.0.4 32 | async-timeout==4.0.3 33 | atomicwrites==1.4.0 34 | attrs==23.1.0 35 | Automat==20.2.0 36 | autopep8==2.0.4 37 | Babel==2.11.0 38 | backcall==0.2.0 39 | backports.functools-lru-cache==1.6.4 40 | backports.tempfile==1.0 41 | backports.weakref==1.0.post1 42 | bcrypt==3.2.0 43 | beautifulsoup4==4.12.2 44 | binaryornot==0.4.4 45 | bitarray==2.5.1 46 | bkcharts==0.2 47 | black==23.11.0 48 | bleach==4.1.0 49 | blinker==1.6.2 50 | bokeh==3.3.4 51 | boltons==23.0.0 52 | boto3==1.29.1 53 | botocore==1.32.1 54 | Bottleneck==1.3.7 55 | Brotli==1.0.9 56 | brotlipy==0.7.0 57 | cachetools==4.2.2 58 | certifi==2024.2.2 59 | cffi==1.16.0 60 | chardet==4.0.0 61 | charset-normalizer==2.0.4 62 | click==8.1.7 63 | cloudpickle==2.2.1 64 | clyent==1.2.2 65 | colorama==0.4.6 66 | colorcet==3.0.1 67 | comm==0.1.2 68 | conda==24.1.2 69 | conda-build==24.1.2 70 | conda-content-trust==0.2.0 71 | conda_index==0.4.0 72 | conda-libmamba-solver==24.1.0 73 | conda-package-handling==2.2.0 74 | conda_package_streaming==0.9.0 75 | conda-repo-cli==1.0.75 76 | conda-token==0.4.0 77 | conda-verify==3.4.2 78 | constantly==23.10.4 79 | contourpy==1.2.0 80 | cookiecutter==2.5.0 81 | cryptography==42.0.2 82 | cssselect==1.2.0 83 | cycler==0.11.0 84 | Cython==0.29.35 85 | cytoolz==0.12.2 86 | daal4py==2023.1.1 87 | dask==2023.11.0 88 | datashader==0.16.0 89 | datashape==0.5.4 90 | debugpy==1.6.7 91 | decorator==5.1.1 92 | defusedxml==0.7.1 93 | diff-match-patch==20200713 94 | dill==0.3.7 95 | distributed==2023.11.0 96 | distro==1.8.0 97 | docstring-to-markdown==0.11 98 | docutils==0.18.1 99 | entrypoints==0.4 100 | et-xmlfile==1.1.0 101 | exceptiongroup==1.2.0 102 | executing==0.8.3 103 | fastjsonschema==2.16.2 104 | filelock==3.13.1 105 | FixedEffectModelPyHDFE==0.0.5 106 | flake8==6.1.0 107 | Flask==2.2.5 108 | fonttools==4.25.0 109 | formulaic==0.6.3 110 | frozenlist==1.4.0 111 | fsspec==2023.10.0 112 | future==0.18.3 113 | gensim==4.3.0 114 | gitdb==4.0.7 115 | GitPython==3.1.37 116 | glob2==0.7 117 | gmpy2==2.1.2 118 | greenlet==3.0.1 119 | h5py==3.9.0 120 | HeapDict==1.0.1 121 | holoviews==1.18.3 122 | huggingface-hub==0.17.3 123 | hvplot==0.9.2 124 | hyperlink==21.0.0 125 | idna==3.4 126 | imagecodecs==2023.1.23 127 | imageio==2.33.1 128 | imagesize==1.4.1 129 | imbalanced-learn==0.11.0 130 | importlib-metadata==7.0.1 131 | importlib-resources==6.1.1 132 | incremental==21.3.0 133 | inflection==0.5.1 134 | iniconfig==1.1.1 135 | intake==0.6.8 136 | interface-meta==1.3.0 137 | interpolation==2.2.4 138 | intervaltree==3.1.0 139 | ipykernel==6.28.0 140 | ipython==8.15.0 141 | ipython-genutils==0.2.0 142 | ipywidgets==8.0.4 143 | isort==5.9.3 144 | itemadapter==0.3.0 145 | itemloaders==1.0.4 146 | itsdangerous==2.0.1 147 | jaraco.classes==3.2.1 148 | jdcal==1.4.1 149 | jedi==0.18.1 150 | jellyfish==1.0.1 151 | Jinja2==3.1.3 152 | jinja2-time==0.2.0 153 | jmespath==1.0.1 154 | joblib==1.2.0 155 | json5==0.9.6 156 | jsonpatch==1.32 157 | jsonpointer==2.1 158 | jsonschema==4.19.2 159 | jsonschema-specifications==2023.7.1 160 | jupyter==1.0.0 161 | jupyter_client==8.6.0 162 | jupyter-console==6.6.3 163 | jupyter_core==5.5.0 164 | jupyter-events==0.8.0 165 | jupyter-lsp==2.2.0 166 | jupyter_server==2.10.0 167 | jupyter_server_fileid==0.9.0 168 | jupyter-server-mathjax==0.2.6 169 | jupyter_server_terminals==0.4.4 170 | jupyter_server_ydoc==0.8.0 171 | jupyter-ydoc==0.2.4 172 | jupyterlab==4.0.11 173 | jupyterlab_git==0.50.0 174 | jupyterlab-pygments==0.1.2 175 | jupyterlab_server==2.25.1 176 | jupyterlab-widgets==3.0.9 177 | keyring==23.13.1 178 | kiwisolver==1.4.4 179 | lazy_loader==0.3 180 | lazy-object-proxy==1.6.0 181 | lckr_jupyterlab_variableinspector==3.1.0 182 | libarchive-c==2.9 183 | libmambapy==1.5.6 184 | linearmodels==5.1 185 | linkify-it-py==2.0.0 186 | llvmlite==0.42.0 187 | lmdb==1.4.1 188 | locket==1.0.0 189 | lxml==4.9.3 190 | lz4==4.3.2 191 | Markdown==3.4.1 192 | markdown-it-py==2.2.0 193 | MarkupSafe==2.1.3 194 | matplotlib==3.8.0 195 | matplotlib-inline==0.1.6 196 | mccabe==0.7.0 197 | mdit-py-plugins==0.3.0 198 | mdurl==0.1.0 199 | menuinst==2.0.2 200 | mistune==2.0.4 201 | mkl-fft==1.3.1 202 | mkl-random==1.2.2 203 | mkl-service==2.4.0 204 | more-itertools==10.1.0 205 | mpmath==1.3.0 206 | msgpack==1.0.3 207 | multidict==6.0.4 208 | multipledispatch==0.6.0 209 | munkres==1.1.4 210 | mypy==1.8.0 211 | mypy-extensions==1.0.0 212 | navigator-updater==0.4.0 213 | nbclassic==1.0.0 214 | nbclient==0.8.0 215 | nbconvert==7.10.0 216 | nbdime==4.0.1 217 | nbformat==5.9.2 218 | nest-asyncio==1.6.0 219 | networkx==3.1 220 | nltk==3.8.1 221 | nose==1.3.7 222 | notebook==7.0.8 223 | notebook_shim==0.2.3 224 | numba==0.59.0 225 | numexpr==2.8.7 226 | numpy==1.26.3 227 | numpydoc==1.5.0 228 | olefile==0.46 229 | openpyxl==3.0.10 230 | overrides==7.4.0 231 | packaging==21.3 232 | pandas==2.1.4 233 | pandocfilters==1.5.0 234 | panel==1.3.8 235 | param==2.0.2 236 | parsel==1.8.1 237 | parso==0.8.3 238 | partd==1.4.1 239 | pathlib==1.0.1 240 | pathspec==0.10.3 241 | patsy==0.5.3 242 | pep8==1.7.1 243 | pexpect==4.8.0 244 | pickleshare==0.7.5 245 | pillow==10.2.0 246 | pip==23.3.1 247 | pkce==1.0.3 248 | pkginfo==1.9.6 249 | platformdirs==3.10.0 250 | plotly==5.9.0 251 | pluggy==1.0.0 252 | ply==3.11 253 | pooch==1.7.0 254 | poyo==0.5.0 255 | prometheus-client==0.14.1 256 | prompt-toolkit==3.0.43 257 | Protego==0.1.16 258 | protobuf==3.20.3 259 | psutil==5.9.0 260 | ptyprocess==0.7.0 261 | pure-eval==0.2.2 262 | py==1.11.0 263 | py-cpuinfo==9.0.0 264 | pyarrow==14.0.2 265 | pyasn1==0.4.8 266 | pyasn1-modules==0.2.8 267 | pycodestyle==2.11.1 268 | pycosat==0.6.6 269 | pycparser==2.21 270 | pyct==0.5.0 271 | pycurl==7.45.2 272 | pydantic==1.10.12 273 | pydeck==0.7.1 274 | PyDispatcher==2.0.5 275 | pydocstyle==6.3.0 276 | pyerfa==2.0.0 277 | pyflakes==3.1.0 278 | Pygments==2.15.1 279 | pyhdfe==0.1.2 280 | PyJWT==2.4.0 281 | pylint==2.16.2 282 | pylint-venv==3.0.3 283 | pyls-spyder==0.4.0 284 | Pympler==0.9 285 | pyobjc-core==9.0 286 | pyobjc-framework-Cocoa==9.0 287 | pyobjc-framework-CoreServices==9.0 288 | pyobjc-framework-FSEvents==9.0 289 | pyodbc==5.0.1 290 | pyOpenSSL==24.0.0 291 | pyparsing==3.0.9 292 | PyQt5==5.15.10 293 | PyQt5-sip==12.13.0 294 | PyQtWebEngine==5.15.6 295 | pyrsistent==0.20.0 296 | PySocks==1.7.1 297 | pytest==7.4.0 298 | python-dateutil==2.8.2 299 | python-dotenv==0.21.0 300 | python-json-logger==2.0.7 301 | python-lsp-black==1.2.1 302 | python-lsp-jsonrpc==1.1.2 303 | python-lsp-server==1.9.0 304 | python-slugify==5.0.2 305 | python-snappy==0.6.1 306 | pytoolconfig==1.2.6 307 | pytz==2023.3.post1 308 | pyviz_comms==3.0.0 309 | pywavelets==1.5.0 310 | PyYAML==6.0.1 311 | pyzmq==25.1.2 312 | QDarkStyle==3.2.3 313 | qstylizer==0.2.2 314 | QtAwesome==1.2.2 315 | qtconsole==5.5.0 316 | QtPy==2.4.1 317 | quantecon==0.7.0 318 | queuelib==1.6.2 319 | referencing==0.30.2 320 | regex==2023.10.3 321 | requests==2.31.0 322 | requests-file==1.5.1 323 | requests-toolbelt==1.0.0 324 | rfc3339-validator==0.1.4 325 | rfc3986-validator==0.1.1 326 | rich==13.3.5 327 | rope==1.7.0 328 | rpds-py==0.10.6 329 | Rtree==1.0.1 330 | ruamel.yaml==0.17.21 331 | ruamel.yaml.clib==0.2.6 332 | ruamel-yaml-conda==0.17.21 333 | s3fs==2023.10.0 334 | s3transfer==0.7.0 335 | scikit-image==0.22.0 336 | scikit-learn==1.3.0 337 | scikit-learn-intelex==20230426.61523 338 | scipy==1.11.4 339 | Scrapy==2.8.0 340 | seaborn==0.12.2 341 | semver==2.13.0 342 | Send2Trash==1.8.2 343 | service-identity==18.1.0 344 | setuptools==68.2.2 345 | setuptools-scm==7.1.0 346 | sip==6.7.12 347 | six==1.16.0 348 | smart-open==5.2.1 349 | smmap==4.0.0 350 | sniffio==1.3.0 351 | snowballstemmer==2.2.0 352 | sortedcollections==2.1.0 353 | sortedcontainers==2.4.0 354 | soupsieve==2.5 355 | Sphinx==5.0.2 356 | sphinxcontrib-applehelp==1.0.2 357 | sphinxcontrib-devhelp==1.0.2 358 | sphinxcontrib-htmlhelp==2.0.0 359 | sphinxcontrib-jsmath==1.0.1 360 | sphinxcontrib-qthelp==1.0.3 361 | sphinxcontrib-serializinghtml==1.1.5 362 | spyder==5.5.0 363 | spyder-kernels==2.5.0 364 | SQLAlchemy==2.0.25 365 | stack-data==0.2.0 366 | stata-setup==0.1.3 367 | statsmodels==0.14.0 368 | streamlit==1.22.0 369 | sympy==1.12 370 | tables==3.9.2 371 | tabulate==0.9.0 372 | TBB==0.2 373 | tblib==1.7.0 374 | Tempita==0.5.3.dev0 375 | tenacity==8.2.2 376 | terminado==0.17.1 377 | testpath==0.6.0 378 | text-unidecode==1.3 379 | textdistance==4.2.1 380 | threadpoolctl==2.2.0 381 | three-merge==0.1.1 382 | tifffile==2023.4.12 383 | tinycss2==1.2.1 384 | tldextract==3.2.0 385 | tokenizers==0.13.3 386 | toml==0.10.2 387 | tomli==2.0.1 388 | tomlkit==0.11.1 389 | toolz==0.12.0 390 | torch==1.13.1 391 | tornado==6.3.3 392 | tqdm==4.65.0 393 | traitlets==5.7.1 394 | transformers==4.24.0 395 | Twisted==22.10.0 396 | typing_extensions==4.9.0 397 | tzdata==2023.3 398 | tzlocal==2.1 399 | uc-micro-py==1.0.1 400 | ujson==5.4.0 401 | Unidecode==1.2.0 402 | urllib3==1.26.18 403 | validators==0.18.2 404 | w3lib==1.21.0 405 | watchdog==2.1.6 406 | wcwidth==0.2.5 407 | webencodings==0.5.1 408 | websocket-client==0.58.0 409 | Werkzeug==2.2.3 410 | whatthepatch==1.0.2 411 | wheel==0.41.2 412 | widgetsnbextension==4.0.5 413 | wrapt==1.14.1 414 | wurlitzer==3.0.2 415 | xarray==2023.6.0 416 | xlrd==2.0.1 417 | XlsxWriter==3.1.1 418 | xlwings==0.29.1 419 | xyzservices==2022.9.0 420 | y-py==0.5.9 421 | yapf==0.40.1 422 | yarl==1.9.3 423 | ypy-websocket==0.8.2 424 | zict==3.0.0 425 | zipp==3.17.0 426 | zope.interface==5.4.0 427 | zstandard==0.19.0 428 | -------------------------------------------------------------------------------- /Hopenhayn/Version 2/vf_hopehaynv2.pdf: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/hessjacob/Quantitative-Macro-Models/f548ac496dc0d440a5ecb251e01b84e4c0580641/Hopenhayn/Version 2/vf_hopehaynv2.pdf -------------------------------------------------------------------------------- /Hopenhayn/Version 3/pdf_hopehaynv3.pdf: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/hessjacob/Quantitative-Macro-Models/f548ac496dc0d440a5ecb251e01b84e4c0580641/Hopenhayn/Version 3/pdf_hopehaynv3.pdf -------------------------------------------------------------------------------- /Hopenhayn/Version 3/pol_inv_hopehaynv3.pdf: -------------------------------------------------------------------------------- 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-------------------------------------------------------------------------------- 1 | 2 | #To install run 3 | # pip install -r /your path/requirements.txt 4 | 5 | 6 | aiobotocore==2.7.0 7 | aiofiles==22.1.0 8 | aiohttp==3.9.3 9 | aioitertools==0.7.1 10 | aiosignal==1.2.0 11 | aiosqlite==0.18.0 12 | alabaster==0.7.12 13 | altair==4.2.2 14 | anaconda-anon-usage==0.4.3 15 | anaconda-client==1.12.2 16 | anaconda-cloud-auth==0.1.4 17 | anaconda-navigator==2.5.2 18 | anyio==4.2.0 19 | appdirs==1.4.4 20 | applaunchservices==0.3.0 21 | appnope==0.1.2 22 | appscript==1.1.2 23 | archspec==0.2.1 24 | argon2-cffi==21.3.0 25 | argon2-cffi-bindings==21.2.0 26 | arrow==1.2.3 27 | astor==0.8.1 28 | astroid==2.14.2 29 | astropy==5.3.4 30 | asttokens==2.0.5 31 | async-lru==2.0.4 32 | async-timeout==4.0.3 33 | atomicwrites==1.4.0 34 | attrs==23.1.0 35 | Automat==20.2.0 36 | autopep8==2.0.4 37 | Babel==2.11.0 38 | backcall==0.2.0 39 | backports.functools-lru-cache==1.6.4 40 | backports.tempfile==1.0 41 | backports.weakref==1.0.post1 42 | bcrypt==3.2.0 43 | beautifulsoup4==4.12.2 44 | binaryornot==0.4.4 45 | bitarray==2.5.1 46 | bkcharts==0.2 47 | black==23.11.0 48 | bleach==4.1.0 49 | blinker==1.6.2 50 | bokeh==3.3.4 51 | boltons==23.0.0 52 | boto3==1.29.1 53 | botocore==1.32.1 54 | Bottleneck==1.3.7 55 | Brotli==1.0.9 56 | brotlipy==0.7.0 57 | cachetools==4.2.2 58 | certifi==2024.2.2 59 | cffi==1.16.0 60 | chardet==4.0.0 61 | charset-normalizer==2.0.4 62 | click==8.1.7 63 | cloudpickle==2.2.1 64 | clyent==1.2.2 65 | colorama==0.4.6 66 | colorcet==3.0.1 67 | comm==0.1.2 68 | conda==24.1.2 69 | conda-build==24.1.2 70 | conda-content-trust==0.2.0 71 | conda_index==0.4.0 72 | conda-libmamba-solver==24.1.0 73 | conda-package-handling==2.2.0 74 | conda_package_streaming==0.9.0 75 | conda-repo-cli==1.0.75 76 | conda-token==0.4.0 77 | conda-verify==3.4.2 78 | constantly==23.10.4 79 | contourpy==1.2.0 80 | cookiecutter==2.5.0 81 | cryptography==42.0.2 82 | cssselect==1.2.0 83 | cycler==0.11.0 84 | Cython==0.29.35 85 | cytoolz==0.12.2 86 | daal4py==2023.1.1 87 | dask==2023.11.0 88 | datashader==0.16.0 89 | datashape==0.5.4 90 | debugpy==1.6.7 91 | decorator==5.1.1 92 | defusedxml==0.7.1 93 | diff-match-patch==20200713 94 | dill==0.3.7 95 | distributed==2023.11.0 96 | distro==1.8.0 97 | docstring-to-markdown==0.11 98 | docutils==0.18.1 99 | entrypoints==0.4 100 | et-xmlfile==1.1.0 101 | exceptiongroup==1.2.0 102 | executing==0.8.3 103 | fastjsonschema==2.16.2 104 | filelock==3.13.1 105 | FixedEffectModelPyHDFE==0.0.5 106 | flake8==6.1.0 107 | Flask==2.2.5 108 | fonttools==4.25.0 109 | formulaic==0.6.3 110 | frozenlist==1.4.0 111 | fsspec==2023.10.0 112 | future==0.18.3 113 | gensim==4.3.0 114 | gitdb==4.0.7 115 | GitPython==3.1.37 116 | glob2==0.7 117 | gmpy2==2.1.2 118 | greenlet==3.0.1 119 | h5py==3.9.0 120 | HeapDict==1.0.1 121 | holoviews==1.18.3 122 | huggingface-hub==0.17.3 123 | hvplot==0.9.2 124 | hyperlink==21.0.0 125 | idna==3.4 126 | imagecodecs==2023.1.23 127 | imageio==2.33.1 128 | imagesize==1.4.1 129 | imbalanced-learn==0.11.0 130 | importlib-metadata==7.0.1 131 | importlib-resources==6.1.1 132 | incremental==21.3.0 133 | inflection==0.5.1 134 | iniconfig==1.1.1 135 | intake==0.6.8 136 | interface-meta==1.3.0 137 | interpolation==2.2.4 138 | intervaltree==3.1.0 139 | ipykernel==6.28.0 140 | ipython==8.15.0 141 | ipython-genutils==0.2.0 142 | ipywidgets==8.0.4 143 | isort==5.9.3 144 | itemadapter==0.3.0 145 | itemloaders==1.0.4 146 | itsdangerous==2.0.1 147 | jaraco.classes==3.2.1 148 | jdcal==1.4.1 149 | jedi==0.18.1 150 | jellyfish==1.0.1 151 | Jinja2==3.1.3 152 | jinja2-time==0.2.0 153 | jmespath==1.0.1 154 | joblib==1.2.0 155 | json5==0.9.6 156 | jsonpatch==1.32 157 | jsonpointer==2.1 158 | jsonschema==4.19.2 159 | jsonschema-specifications==2023.7.1 160 | jupyter==1.0.0 161 | jupyter_client==8.6.0 162 | jupyter-console==6.6.3 163 | jupyter_core==5.5.0 164 | jupyter-events==0.8.0 165 | jupyter-lsp==2.2.0 166 | jupyter_server==2.10.0 167 | jupyter_server_fileid==0.9.0 168 | jupyter-server-mathjax==0.2.6 169 | jupyter_server_terminals==0.4.4 170 | jupyter_server_ydoc==0.8.0 171 | jupyter-ydoc==0.2.4 172 | jupyterlab==4.0.11 173 | jupyterlab_git==0.50.0 174 | jupyterlab-pygments==0.1.2 175 | jupyterlab_server==2.25.1 176 | jupyterlab-widgets==3.0.9 177 | keyring==23.13.1 178 | kiwisolver==1.4.4 179 | lazy_loader==0.3 180 | lazy-object-proxy==1.6.0 181 | lckr_jupyterlab_variableinspector==3.1.0 182 | libarchive-c==2.9 183 | libmambapy==1.5.6 184 | linearmodels==5.1 185 | linkify-it-py==2.0.0 186 | llvmlite==0.42.0 187 | lmdb==1.4.1 188 | locket==1.0.0 189 | lxml==4.9.3 190 | lz4==4.3.2 191 | Markdown==3.4.1 192 | markdown-it-py==2.2.0 193 | MarkupSafe==2.1.3 194 | matplotlib==3.8.0 195 | matplotlib-inline==0.1.6 196 | mccabe==0.7.0 197 | mdit-py-plugins==0.3.0 198 | mdurl==0.1.0 199 | menuinst==2.0.2 200 | mistune==2.0.4 201 | mkl-fft==1.3.1 202 | mkl-random==1.2.2 203 | mkl-service==2.4.0 204 | more-itertools==10.1.0 205 | mpmath==1.3.0 206 | msgpack==1.0.3 207 | multidict==6.0.4 208 | multipledispatch==0.6.0 209 | munkres==1.1.4 210 | mypy==1.8.0 211 | mypy-extensions==1.0.0 212 | navigator-updater==0.4.0 213 | nbclassic==1.0.0 214 | nbclient==0.8.0 215 | nbconvert==7.10.0 216 | nbdime==4.0.1 217 | nbformat==5.9.2 218 | nest-asyncio==1.6.0 219 | networkx==3.1 220 | nltk==3.8.1 221 | nose==1.3.7 222 | notebook==7.0.8 223 | notebook_shim==0.2.3 224 | numba==0.59.0 225 | numexpr==2.8.7 226 | numpy==1.26.3 227 | numpydoc==1.5.0 228 | olefile==0.46 229 | openpyxl==3.0.10 230 | overrides==7.4.0 231 | packaging==21.3 232 | pandas==2.1.4 233 | pandocfilters==1.5.0 234 | panel==1.3.8 235 | param==2.0.2 236 | parsel==1.8.1 237 | parso==0.8.3 238 | partd==1.4.1 239 | pathlib==1.0.1 240 | pathspec==0.10.3 241 | patsy==0.5.3 242 | pep8==1.7.1 243 | pexpect==4.8.0 244 | pickleshare==0.7.5 245 | pillow==10.2.0 246 | pip==23.3.1 247 | pkce==1.0.3 248 | pkginfo==1.9.6 249 | platformdirs==3.10.0 250 | plotly==5.9.0 251 | pluggy==1.0.0 252 | ply==3.11 253 | pooch==1.7.0 254 | poyo==0.5.0 255 | prometheus-client==0.14.1 256 | prompt-toolkit==3.0.43 257 | Protego==0.1.16 258 | protobuf==3.20.3 259 | psutil==5.9.0 260 | ptyprocess==0.7.0 261 | pure-eval==0.2.2 262 | py==1.11.0 263 | py-cpuinfo==9.0.0 264 | pyarrow==14.0.2 265 | pyasn1==0.4.8 266 | pyasn1-modules==0.2.8 267 | pycodestyle==2.11.1 268 | pycosat==0.6.6 269 | pycparser==2.21 270 | pyct==0.5.0 271 | pycurl==7.45.2 272 | pydantic==1.10.12 273 | pydeck==0.7.1 274 | PyDispatcher==2.0.5 275 | pydocstyle==6.3.0 276 | pyerfa==2.0.0 277 | pyflakes==3.1.0 278 | Pygments==2.15.1 279 | pyhdfe==0.1.2 280 | PyJWT==2.4.0 281 | pylint==2.16.2 282 | pylint-venv==3.0.3 283 | pyls-spyder==0.4.0 284 | Pympler==0.9 285 | pyobjc-core==9.0 286 | pyobjc-framework-Cocoa==9.0 287 | pyobjc-framework-CoreServices==9.0 288 | pyobjc-framework-FSEvents==9.0 289 | pyodbc==5.0.1 290 | pyOpenSSL==24.0.0 291 | pyparsing==3.0.9 292 | PyQt5==5.15.10 293 | PyQt5-sip==12.13.0 294 | PyQtWebEngine==5.15.6 295 | pyrsistent==0.20.0 296 | PySocks==1.7.1 297 | pytest==7.4.0 298 | python-dateutil==2.8.2 299 | python-dotenv==0.21.0 300 | python-json-logger==2.0.7 301 | python-lsp-black==1.2.1 302 | python-lsp-jsonrpc==1.1.2 303 | python-lsp-server==1.9.0 304 | python-slugify==5.0.2 305 | python-snappy==0.6.1 306 | pytoolconfig==1.2.6 307 | pytz==2023.3.post1 308 | pyviz_comms==3.0.0 309 | pywavelets==1.5.0 310 | PyYAML==6.0.1 311 | pyzmq==25.1.2 312 | QDarkStyle==3.2.3 313 | qstylizer==0.2.2 314 | QtAwesome==1.2.2 315 | qtconsole==5.5.0 316 | QtPy==2.4.1 317 | quantecon==0.7.0 318 | queuelib==1.6.2 319 | referencing==0.30.2 320 | regex==2023.10.3 321 | requests==2.31.0 322 | requests-file==1.5.1 323 | requests-toolbelt==1.0.0 324 | rfc3339-validator==0.1.4 325 | rfc3986-validator==0.1.1 326 | rich==13.3.5 327 | rope==1.7.0 328 | rpds-py==0.10.6 329 | Rtree==1.0.1 330 | ruamel.yaml==0.17.21 331 | ruamel.yaml.clib==0.2.6 332 | ruamel-yaml-conda==0.17.21 333 | s3fs==2023.10.0 334 | s3transfer==0.7.0 335 | scikit-image==0.22.0 336 | scikit-learn==1.3.0 337 | scikit-learn-intelex==20230426.61523 338 | scipy==1.11.4 339 | Scrapy==2.8.0 340 | seaborn==0.12.2 341 | semver==2.13.0 342 | Send2Trash==1.8.2 343 | service-identity==18.1.0 344 | setuptools==68.2.2 345 | setuptools-scm==7.1.0 346 | sip==6.7.12 347 | six==1.16.0 348 | smart-open==5.2.1 349 | smmap==4.0.0 350 | sniffio==1.3.0 351 | snowballstemmer==2.2.0 352 | sortedcollections==2.1.0 353 | sortedcontainers==2.4.0 354 | soupsieve==2.5 355 | Sphinx==5.0.2 356 | sphinxcontrib-applehelp==1.0.2 357 | sphinxcontrib-devhelp==1.0.2 358 | sphinxcontrib-htmlhelp==2.0.0 359 | sphinxcontrib-jsmath==1.0.1 360 | sphinxcontrib-qthelp==1.0.3 361 | sphinxcontrib-serializinghtml==1.1.5 362 | spyder==5.5.0 363 | spyder-kernels==2.5.0 364 | SQLAlchemy==2.0.25 365 | stack-data==0.2.0 366 | stata-setup==0.1.3 367 | statsmodels==0.14.0 368 | streamlit==1.22.0 369 | sympy==1.12 370 | tables==3.9.2 371 | tabulate==0.9.0 372 | TBB==0.2 373 | tblib==1.7.0 374 | Tempita==0.5.3.dev0 375 | tenacity==8.2.2 376 | terminado==0.17.1 377 | testpath==0.6.0 378 | text-unidecode==1.3 379 | textdistance==4.2.1 380 | threadpoolctl==2.2.0 381 | three-merge==0.1.1 382 | tifffile==2023.4.12 383 | tinycss2==1.2.1 384 | tldextract==3.2.0 385 | tokenizers==0.13.3 386 | toml==0.10.2 387 | tomli==2.0.1 388 | tomlkit==0.11.1 389 | toolz==0.12.0 390 | torch==1.13.1 391 | tornado==6.3.3 392 | tqdm==4.65.0 393 | traitlets==5.7.1 394 | transformers==4.24.0 395 | Twisted==22.10.0 396 | typing_extensions==4.9.0 397 | tzdata==2023.3 398 | tzlocal==2.1 399 | uc-micro-py==1.0.1 400 | ujson==5.4.0 401 | Unidecode==1.2.0 402 | urllib3==1.26.18 403 | validators==0.18.2 404 | w3lib==1.21.0 405 | watchdog==2.1.6 406 | wcwidth==0.2.5 407 | webencodings==0.5.1 408 | websocket-client==0.58.0 409 | Werkzeug==2.2.3 410 | whatthepatch==1.0.2 411 | wheel==0.41.2 412 | widgetsnbextension==4.0.5 413 | wrapt==1.14.1 414 | wurlitzer==3.0.2 415 | xarray==2023.6.0 416 | xlrd==2.0.1 417 | XlsxWriter==3.1.1 418 | xlwings==0.29.1 419 | xyzservices==2022.9.0 420 | y-py==0.5.9 421 | yapf==0.40.1 422 | yarl==1.9.3 423 | ypy-websocket==0.8.2 424 | zict==3.0.0 425 | zipp==3.17.0 426 | zope.interface==5.4.0 427 | zstandard==0.19.0 428 | -------------------------------------------------------------------------------- /Hopenhayn/Version 3/vf_hopehaynv3.pdf: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/hessjacob/Quantitative-Macro-Models/f548ac496dc0d440a5ecb251e01b84e4c0580641/Hopenhayn/Version 3/vf_hopehaynv3.pdf -------------------------------------------------------------------------------- /Neoclassical Growth/Matlab/markov.m: -------------------------------------------------------------------------------- 1 | function [chain,state]=markov(PI,s,n,s0,seed); 2 | % 3 | % [chain,state]=markov(PI,s,n,s0); 4 | % 5 | % simulate a Markov chain 6 | % 7 | % PI : Transition matrix 8 | % s : State vector 9 | % n : length of simulation 10 | % s0 : initial state (index) 11 | % 12 | % chain : values for the simulated markov chain 13 | % state : index of the state 14 | % 15 | [rpi,cpi]=size(PI); 16 | s=s(:); 17 | if ~(rpi==cpi); 18 | error('Transition matrix must be square') 19 | end 20 | if ~(size(s,1)==cpi); 21 | error('Number of state does not match size of Transition matrix') 22 | end 23 | 24 | cum_PI=[zeros(rpi,1) cumsum(PI')']; 25 | 26 | if nargin<4; 27 | s0=1; 28 | end 29 | 30 | if nargin>4; 31 | rand('state',seed) 32 | end 33 | sim = rand(n,1); 34 | state = zeros(n,1); 35 | state(1) = s0; 36 | % 37 | for k=2:n; 38 | state(k)=find(((sim(k)<=cum_PI(state(k-1),2:cpi+1))&(sim(k)>cum_PI(state(k-1),1:cpi)))); %the state is determined if less than 1) the cumulative sum of the state and 2) if it is greater than the cumulative sum of 0 state1, state2. will take the state which both have ones. 39 | end; 40 | chain=s(state); -------------------------------------------------------------------------------- /Neoclassical Growth/Matlab/neoclassical_growth_deterministic.m: -------------------------------------------------------------------------------- 1 | 2 | %Neoclassical Growth Model -- Deterministic Simulation 3 | 4 | %Author: Jacob Hess 5 | 6 | %I wrote this code as a personal learning exercise and it is not optimized. 7 | % The first part solve the deterministic neoclassical growth model by discretized value 8 | % function iteration. The second part in order to make the savings policy function continuous 9 | % I approximate it with chebyshev polynomials and minimize the residuals between 10 | % the interpolants and the true function with ordinary least squares. I 11 | % then simulate the model by setting initial capital below the steady state 12 | % and plot the transition 13 | 14 | %To make the policy function continuous you can choose to use cubic spline 15 | %or chebshev interpolation. Chebyshev will work a lot better. 16 | 17 | %References that I used for this code are 18 | %1) Chapter 5 in RBCs of ABCs by George McCandless 19 | %2) Value Itertion Notes by Fabrice Collard (http://fabcol.free.fr/notes.html) 20 | %3) Eric Sims Value Function Iteration Notes (https://www3.nd.edu/~esims1/grad_macro_17.html) 21 | %4) Raul Santaeulalia-Llopis Function Approximation Notes (http://r-santaeulalia.net/Quantitative-Macro-F19-UnitI.html) 22 | 23 | clear 24 | close all 25 | clc 26 | set(0, 'DefaultLineLineWidth', 1.5); 27 | 28 | tic 29 | 30 | global v0 beta delta alpha kmat k0 s 31 | 32 | plott=1; % set to 1 to see plots 33 | approx = 2; %set 1 for cubic spline. set 2 for chebyshev. 34 | 35 | % set parameters. This model uses log utility. 36 | alpha = 0.33; % capital's share 37 | beta = 0.95; 38 | delta = 0.1; % depreciation rate (annual) 39 | s=1.5; %sigma 40 | 41 | %Steady State 42 | kgrid = 99; % grid points + 1 43 | kstar = (alpha/(1/beta - (1-delta)))^(1/(1 - alpha)); % steady state k 44 | cstar = kstar^(alpha) - delta*kstar; 45 | istar = delta*kstar; 46 | ystar = kstar^(alpha); 47 | consgrowthstar=beta*(alpha*kstar.^(alpha-1) + (1-delta)).^(1/s); 48 | 49 | %Constructing grid and grid ranges 50 | kmin = 0.25*kstar; 51 | kmax = 1.75*kstar; 52 | grid = (kmax-kmin)/kgrid; 53 | 54 | kmat = kmin:grid:kmax; 55 | kmat = kmat'; 56 | N = length(kmat); 57 | 58 | polfun_k1 = zeros(kgrid+1,1); 59 | 60 | tol = 0.01; %tolerance 61 | maxits = 1000; %maximum number of allowed iterations 62 | dif = tol+1000; 63 | its=1; %starting iteration value 64 | v0=zeros(N,1); %initial guess 65 | 66 | while dif>tol && its < maxits 67 | for i=1:N 68 | k0 = kmat(i,1); %Takes each value i in the state space from the grid 69 | k1 = fminbnd(@valfun_det,kmin,kmax); %Arg max of bellman equation (in function file). Fminbind finds the minimum on the specific interval. Our interval is the grid limits. 70 | v1(i,1) = - valfun_det(k1); %Takes new optimized values for i and plugs it into the new function 71 | polfun_k1(i,1) = k1; %Finds the policy function associated with k1 72 | end 73 | V_store(:,its) = v1; %storing the value functions to store value functions over the iterations. This is to plot the convergence 74 | dif = norm(v1-v0); %measure the euclidean distance between value functions 75 | v0=v1; % if dif>tol then v1 because the v0 for the function file to do the iteration again 76 | its= its+1; %iteration counter 77 | end 78 | 79 | %loop for write the consumption policy function 80 | for i=1:N 81 | polfun_cons(i,1) = kmat(i,1)^(alpha) - polfun_k1(i,1) + (1-delta)*kmat(i,1); 82 | end 83 | 84 | %Plotting value and policy functions 85 | if plott==1 86 | figure 87 | plot(kmat,v1,'k','Linewidth',1) 88 | title('Final Value Function') 89 | xlabel('k') 90 | ylabel('V(k)') 91 | 92 | figure 93 | plot(kmat,polfun_k1,'k','Linewidth',1) 94 | title('k_{t+1} Policy Function') 95 | xlabel('k_t') 96 | ylabel('k_{t+1}') 97 | ssline = refline([1 0]); 98 | ssline.LineStyle = '--'; 99 | legend('Policy Function','45 degree line','Location','Best') 100 | 101 | figure 102 | plot(kmat,polfun_cons,'k','Linewidth',1) 103 | title('Consumption Policy function') 104 | xlabel('k') 105 | ylabel('c') 106 | 107 | %plot the value function convergence 108 | figure 109 | hold on 110 | for k = 1:9:its 111 | txt = ['Iteration ',num2str(k)]; 112 | plot(kmat,V_store(:,k),'DisplayName',txt) 113 | end 114 | legend show 115 | set(legend,'location','northwest') 116 | xlabel('k_t') 117 | ylabel('v(k)') 118 | title('Value Function Iterations') 119 | hold off 120 | 121 | end 122 | 123 | %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% 124 | %%%%%%%%% Transition %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% 125 | %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% 126 | 127 | k_init=0.5*kstar; %initial capital 128 | nrep=50; %number of periods to simulate 129 | k=zeros(nrep+1,1); %initialize dynamics 130 | k(1)=k_init; 131 | 132 | if approx == 1 133 | %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% 134 | %%%%%%%%% cubic spline approx %%%%%% 135 | %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% 136 | 137 | 138 | k(1)=k_init; 139 | for t=1:nrep 140 | k(t+1)=interp1(kmat,polfun_k1,k(t),'pchip'); %pchip means cubic interpolation 141 | end 142 | 143 | end 144 | 145 | if approx == 2 146 | %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% 147 | %%%%%%%%% Chebyshev approx %%%%%%%%% 148 | %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% 149 | 150 | %We are working to get the decision rule of form: k' = sum_{l=0} ^n 151 | %\theta_l T_l(\gamma(k)) 152 | 153 | %step 1: Apply this transformation to the data points. This is \gamma(k). 154 | %We are transforming the grid points from [kmin to kmax] to [-1 to 1]. 155 | 156 | 157 | transk = 2*(kmat-kmin)/(kmax-kmin)-1; % EVENLY SPACED nodes. 158 | 159 | % Computes the matrix of Chebychev polynomials 160 | 161 | n = 10; %order approximaztion 162 | Tk = [ones(kgrid+1,1) transk]; %Chebyshev polynomials of order 0 and 1. 163 | %We know that a chebyshev polynomial of order 0 is 1 (column vector of ones). Order 1 is the nodes themselves. 164 | 165 | % Now we carry out the higher order polynomials and store them in their respective column. 166 | % This is the recursive scheme from slide 42 of Raul Santaeulalia's 167 | % Function Approxmiations. 168 | 169 | for i=3:n 170 | Tk=[Tk 2*transk.*Tk(:,i-1)-Tk(:,i-2)]; 171 | end 172 | 173 | %b=Tk\polfun_k1; %computes OLS 174 | b = inv((Tk'*Tk))*Tk'*polfun_k1; %computes OLS 175 | 176 | 177 | 178 | %plot the discrete (every fifth grid point) vs. the approximated continuous 179 | %function 180 | plot(kmat(1:5:100),polfun_k1(1:5:100),'o',kmat,Tk*b,'-'); 181 | 182 | 183 | %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% 184 | %%%%%%%%% simulation %%%%%%%%%%%%%%% 185 | %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% 186 | 187 | for t=1:nrep 188 | trkt=2*(k(t)-kmin)/(kmax-kmin)-1; 189 | Tk = [1 trkt]; 190 | for i=3:n 191 | Tk=[Tk 2*trkt.*Tk(:,i-1)-Tk(:,i-2)]; 192 | end 193 | k(t+1)=Tk*b; 194 | end 195 | 196 | end 197 | 198 | y=k(1:nrep).^alpha; 199 | i=k(2:nrep+1)-(1-delta)*k(1:nrep); 200 | c=y-i; 201 | 202 | 203 | 204 | figure; 205 | subplot(221);plot(1:nrep,k(1:nrep),1:nrep,kstar*ones(nrep,1),'--'); 206 | set(gca,'fontname','times','fontsize',12); 207 | xlabel('Time','fontname','times','fontsize',12); 208 | title('Capital stock','fontname','times','fontsize',12); 209 | subplot(222);plot(1:nrep,c,1:nrep,cstar*ones(nrep,1),'--'); 210 | set(gca,'fontname','times','fontsize',12); 211 | xlabel('Time','fontname','times','fontsize',12); 212 | title('Consumption','fontname','times','fontsize',12); 213 | subplot(223);plot(1:nrep,y,1:nrep,ystar*ones(nrep,1),'--'); 214 | set(gca,'fontname','times','fontsize',12); 215 | xlabel('Time','fontname','times','fontsize',12); 216 | title('Output','fontname','times','fontsize',12); 217 | subplot(224);plot(1:nrep,i,1:nrep,istar*ones(nrep,1),'--'); 218 | set(gca,'fontname','times','fontsize',12); 219 | xlabel('Time','fontname','times','fontsize',12); 220 | title('Investment','fontname','times','fontsize',12); 221 | 222 | toc 223 | -------------------------------------------------------------------------------- /Neoclassical Growth/Matlab/neoclassical_growth_stochastic.m: -------------------------------------------------------------------------------- 1 | 2 | %Author: Jacob Hess 3 | 4 | %I wrote this code as a personal learning exercise and it is not optimized. 5 | % The first part I solve the stochastic neoclassical growth model by discretized value 6 | % function iteration. The stochastic variable TFP has three different states and 7 | % follows a markov process. The second part in order to make the savings policy function continuous 8 | % I approximate it with chebyshev polynomials and minimize the residuals between 9 | % the interpolants and the true function with ordinary least squares. I 10 | % then simulate the tfp process and the model over 500 periods. 11 | 12 | %References that I used for this code are 13 | %1) Chapter 5 in RBCs of ABCs by George McCandless 14 | %2) Value Itertion Notes by Fabrice Collard (http://fabcol.free.fr/notes.html) 15 | %3) Eric Sims Value Function Iteration Notes (https://www3.nd.edu/~esims1/grad_macro_17.html) 16 | %4) Raul Santaeulalia-Llopis Function Approximation Notes (http://r-santaeulalia.net/Quantitative-Macro-F19-UnitI.html) 17 | 18 | 19 | clear 20 | close all 21 | clc 22 | 23 | tic 24 | 25 | global v0 beta delta alpha kmat k0 prob a0 s j 26 | 27 | plott=1; % set to 1 to see plots 28 | 29 | % set parameters. This model uses log utility. 30 | alpha = 0.33; % capital share 31 | beta = 0.95; 32 | delta = 0.1; % depreciation rate (annual) 33 | s=2; %sigma 34 | 35 | %Steady State 36 | kgrid = 99; % grid points + 1 37 | kstar = (alpha/(1/beta - (1-delta)))^(1/(1 - alpha)); % steady state k 38 | cstar = kstar^(alpha) - delta*kstar; 39 | istar = delta*kstar; 40 | ystar = kstar^(alpha); 41 | consgrowthstar=beta*(alpha*kstar.^(alpha-1) + (1-delta)).^(1/s); 42 | 43 | %Stochastic Process 44 | amat = [0.9 1 1.1]'; 45 | prob = 1/3*ones(3,3); 46 | 47 | %Constructing grid and grid ranges 48 | kmin = 0.25*kstar; 49 | kmax = 1.75*kstar; 50 | grid = (kmax-kmin)/kgrid; 51 | 52 | kmat = kmin:grid:kmax; 53 | kmat = kmat'; 54 | N = length(kmat); 55 | 56 | polfun_cons = zeros(kgrid+1,length(amat)); 57 | 58 | tol = 0.01; %tolerance 59 | maxits = 1000; %maximum number of allowed iterations 60 | dif = tol+1000; 61 | its=1; %starting iteration value 62 | v0=zeros(N,length(amat)); %initial guess 63 | 64 | 65 | 66 | while dif>tol && its < maxits 67 | for j = 1:length(amat) 68 | for i = 1:N 69 | k0 = kmat(i,1); 70 | a0 = amat(j,1); 71 | k1 = fminbnd(@valfun_stoch,kmin,kmax); 72 | v1(i,j) = -valfun_stoch(k1); 73 | polfun_k1(i,j) = k1; 74 | end 75 | end 76 | dif = norm(v1-v0); 77 | v0 = v1; 78 | its = its+1; 79 | end 80 | 81 | %loop for write the consumption policy function 82 | for j = 1:length(amat) 83 | for i=1:N 84 | polfun_cons(i,j) = amat(j,1)*kmat(i,1)^(alpha) - polfun_k1(i,j) + (1-delta)*kmat(i,1); 85 | end 86 | end 87 | 88 | % Plotting value and policy functions 89 | if plott==1 90 | figure; 91 | plot(kmat,v1(:,1),'r',kmat,v1(:,2),'b',kmat,v1(:,3),'g','Linewidth',1) 92 | title('Final Value Function') 93 | xlabel('k_t') 94 | ylabel('V(k)') 95 | legend('A = 0.9','A = 1', 'A = 1.1', 'Location','Best') 96 | 97 | figure; 98 | plot(kmat,polfun_k1(:,1),'r',kmat,polfun_k1(:,2),'b',kmat,polfun_k1(:,3),'g','Linewidth',1) 99 | title('k_{t+1} Policy Function') 100 | xlabel('k_t') 101 | ylabel('k_{t+1}') 102 | legend('A = 0.9','A = 1', 'A = 1.1', 'Location','Best') 103 | 104 | figure; 105 | plot(kmat,polfun_cons(:,1),'r',kmat,polfun_cons(:,2),'b',kmat,polfun_cons(:,3),'g','Linewidth',1) 106 | title('Consumption Policy function') 107 | xlabel('k_t') 108 | ylabel('c_t') 109 | legend('A = 0.9','A = 1', 'A = 1.1', 'Location','Best') 110 | 111 | end 112 | 113 | 114 | 115 | %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% 116 | %%%%%%%%% Chebyshev Approx %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% 117 | %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% 118 | 119 | k_init=0.75*kstar; %initial capital 120 | nrep=200; %number of periods to simulate 121 | k=zeros(nrep+1,1); %initialize dynamics 122 | y=zeros(nrep,1); %some matrices 123 | k(1)=k_init; 124 | 125 | 126 | 127 | transk = 2*(kmat-kmin)/(kmax-kmin)-1; % EVENLY SPACED nodes. 128 | 129 | % Computes the matrix of Chebychev polynomials 130 | 131 | n = 10; %order approximaztion 132 | Tk = [ones(kgrid+1,1) transk]; %Chebyshev polynomials of order 0 and 1. 133 | %We know that a chebyshev polynomial of order 0 is 1 (column vector of ones). Order 1 is the nodes themselves. 134 | 135 | % Now we carry out the higher order polynomials and store them in their respective column. 136 | % This is the recursive scheme from slide 42 of Raul Santaeulalia's 137 | % Function Approxmiations. 138 | 139 | for i=3:n 140 | Tk=[Tk 2*transk.*Tk(:,i-1)-Tk(:,i-2)]; 141 | end 142 | 143 | %b=Tk\polfun_k1; %computes OLS 144 | b = inv((Tk'*Tk))*Tk'*polfun_k1; %computes OLS 145 | 146 | 147 | 148 | 149 | 150 | %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% 151 | %%%%%%%%% simulation %%%%%%%%%%%%%%% 152 | %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% 153 | 154 | 155 | %Run the stochastic simulation of tfp. a is the state. id is the state 156 | %index. 157 | [a,id]=markov(prob,amat,nrep,1,123); 158 | 159 | 160 | 161 | for t=1:nrep 162 | % Prepare the approximation for the capital stock 163 | trkt=2*(k(t)-kmin)/(kmax-kmin)-1; 164 | Tk = [1 trkt]; 165 | for i=3:n 166 | Tk=[Tk 2*trkt.*Tk(:,i-1)-Tk(:,i-2)]; 167 | end 168 | 169 | k(t+1)=Tk*b(:,id(t)); % use the appropriate decision rule 170 | y(t)=amat(id(t))*k(t).^alpha; % computes output with the given tfp for that period. 171 | 172 | end 173 | 174 | 175 | i=k(2:nrep+1)-(1-delta)*k(1:nrep); 176 | c=y-i; 177 | 178 | figure; 179 | subplot(221);plot(1:nrep,k(1:nrep)); 180 | set(gca,'fontname','times','fontsize',12); 181 | xlabel('Time','fontname','times','fontsize',12); 182 | title('Capital stock','fontname','times','fontsize',12); 183 | subplot(222);plot(1:nrep,i); 184 | set(gca,'fontname','times','fontsize',12); 185 | xlabel('Time','fontname','times','fontsize',12); 186 | title('Investment','fontname','times','fontsize',12); 187 | subplot(223);plot(1:nrep,y); 188 | set(gca,'fontname','times','fontsize',12); 189 | xlabel('Time','fontname','times','fontsize',12); 190 | title('Output','fontname','times','fontsize',12); 191 | subplot(224);plot(1:nrep,c); 192 | set(gca,'fontname','times','fontsize',12); 193 | xlabel('Time','fontname','times','fontsize',12); 194 | title('Consumption','fontname','times','fontsize',12); 195 | 196 | 197 | toc 198 | 199 | -------------------------------------------------------------------------------- /Neoclassical Growth/Matlab/valfun_det.m: -------------------------------------------------------------------------------- 1 | function val=valfun_det(k) 2 | 3 | % This program gets the value function for a neoclassical growth model with 4 | % no uncertainty and CRRA utility 5 | 6 | global v0 beta delta alpha kmat k0 s 7 | 8 | g = interp1(kmat,v0,k,'linear'); % smooths out previous value function 9 | 10 | c = k0^alpha - k + (1-delta)*k0; % consumption (This is the constraint) 11 | 12 | if c<0 13 | val = -888888888888888888-800*abs(c); % keeps it from going negative 14 | else 15 | 16 | val=(1/(1-s))*((c)^(1-s)-1) + beta*g; %log utility approximately will produce the same results as s=1.01 17 | 18 | end 19 | val = -val; % make it negative since we're maximizing and code is to minimize. -------------------------------------------------------------------------------- /Neoclassical Growth/Matlab/valfun_stoch.m: -------------------------------------------------------------------------------- 1 | function val=valfun_stoch(k) 2 | 3 | % This program gets the value function for a stochastic growth model with 4 | % CRRA utility 5 | 6 | global v0 beta delta alpha kmat k0 prob a0 s j 7 | 8 | g = interp1(kmat,v0,k,'linear'); % smooths out previous value function 9 | 10 | c = a0*k0^alpha - k + (1-delta)*k0; % consumption 11 | if c <= 0 12 | val = -8888888888888888-800*abs(c); % keeps it from going negative 13 | else 14 | 15 | val = (1/(1-s))*(c^(1-s) -1) + beta*(g*prob(j,:)'); 16 | end; 17 | 18 | val = -val; % make it negative since we're maximizing and code is to minimize. -------------------------------------------------------------------------------- /Neoclassical Growth/Neoclassical Growth.md: -------------------------------------------------------------------------------- 1 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Unless otherwise stated all solution methods are avaliable for both the deterministic and stochastic case. The versions differ in how the social planner problem is solved, but obtain the same solution. See the RBC files which is this model but with elastic labor supply. 3 | 4 | **Solution Methods** 5 | - Value Function Iteration with Discretization 6 | - Value Function Iteration with Linear Interpolation 7 | * Solved with conventional value function interation and interpolates with cubic splines to get the value for the value function between the grid points. The computation requires less iterations and is significantly faster than conventional value function iteration. 8 | 9 | **Code Features** 10 | 11 | 1) The deterministic files conduct a perfect foresight transition to the steady state. 12 | 2) The stochastic files generate a markov chain simulation and evaluate the accuracy of the solution by computing the euler equation errors. 13 | 3) The interpolation scheme (chosen by the user) for the transitions and simulations interpolates the decision rules with either cubic interpolation or by approximating them with chebyshev polynomials and OLS. 14 | 15 | -------------------------------------------------------------------------------- /Neoclassical Growth/Python/Stochastic/VFI + Interpolation/Figures Simulation/capital_sim_ncgm_vfi_interpolate.pdf: -------------------------------------------------------------------------------- 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aioitertools==0.7.1 10 | aiosignal==1.2.0 11 | aiosqlite==0.18.0 12 | alabaster==0.7.12 13 | altair==4.2.2 14 | anaconda-anon-usage==0.4.3 15 | anaconda-client==1.12.2 16 | anaconda-cloud-auth==0.1.4 17 | anaconda-navigator==2.5.2 18 | anyio==4.2.0 19 | appdirs==1.4.4 20 | applaunchservices==0.3.0 21 | appnope==0.1.2 22 | appscript==1.1.2 23 | archspec==0.2.1 24 | argon2-cffi==21.3.0 25 | argon2-cffi-bindings==21.2.0 26 | arrow==1.2.3 27 | astor==0.8.1 28 | astroid==2.14.2 29 | astropy==5.3.4 30 | asttokens==2.0.5 31 | async-lru==2.0.4 32 | async-timeout==4.0.3 33 | atomicwrites==1.4.0 34 | attrs==23.1.0 35 | Automat==20.2.0 36 | autopep8==2.0.4 37 | Babel==2.11.0 38 | backcall==0.2.0 39 | backports.functools-lru-cache==1.6.4 40 | backports.tempfile==1.0 41 | backports.weakref==1.0.post1 42 | bcrypt==3.2.0 43 | beautifulsoup4==4.12.2 44 | binaryornot==0.4.4 45 | bitarray==2.5.1 46 | bkcharts==0.2 47 | black==23.11.0 48 | bleach==4.1.0 49 | blinker==1.6.2 50 | bokeh==3.3.4 51 | boltons==23.0.0 52 | boto3==1.29.1 53 | botocore==1.32.1 54 | Bottleneck==1.3.7 55 | Brotli==1.0.9 56 | brotlipy==0.7.0 57 | cachetools==4.2.2 58 | certifi==2024.2.2 59 | cffi==1.16.0 60 | chardet==4.0.0 61 | charset-normalizer==2.0.4 62 | click==8.1.7 63 | cloudpickle==2.2.1 64 | clyent==1.2.2 65 | colorama==0.4.6 66 | colorcet==3.0.1 67 | comm==0.1.2 68 | conda==24.1.2 69 | conda-build==24.1.2 70 | conda-content-trust==0.2.0 71 | conda_index==0.4.0 72 | conda-libmamba-solver==24.1.0 73 | conda-package-handling==2.2.0 74 | conda_package_streaming==0.9.0 75 | conda-repo-cli==1.0.75 76 | conda-token==0.4.0 77 | conda-verify==3.4.2 78 | constantly==23.10.4 79 | contourpy==1.2.0 80 | cookiecutter==2.5.0 81 | cryptography==42.0.2 82 | cssselect==1.2.0 83 | cycler==0.11.0 84 | Cython==0.29.35 85 | cytoolz==0.12.2 86 | daal4py==2023.1.1 87 | dask==2023.11.0 88 | datashader==0.16.0 89 | datashape==0.5.4 90 | debugpy==1.6.7 91 | decorator==5.1.1 92 | defusedxml==0.7.1 93 | diff-match-patch==20200713 94 | dill==0.3.7 95 | distributed==2023.11.0 96 | distro==1.8.0 97 | docstring-to-markdown==0.11 98 | docutils==0.18.1 99 | entrypoints==0.4 100 | et-xmlfile==1.1.0 101 | exceptiongroup==1.2.0 102 | executing==0.8.3 103 | fastjsonschema==2.16.2 104 | filelock==3.13.1 105 | FixedEffectModelPyHDFE==0.0.5 106 | flake8==6.1.0 107 | Flask==2.2.5 108 | fonttools==4.25.0 109 | formulaic==0.6.3 110 | frozenlist==1.4.0 111 | fsspec==2023.10.0 112 | future==0.18.3 113 | gensim==4.3.0 114 | gitdb==4.0.7 115 | GitPython==3.1.37 116 | glob2==0.7 117 | gmpy2==2.1.2 118 | greenlet==3.0.1 119 | h5py==3.9.0 120 | HeapDict==1.0.1 121 | holoviews==1.18.3 122 | huggingface-hub==0.17.3 123 | hvplot==0.9.2 124 | hyperlink==21.0.0 125 | idna==3.4 126 | imagecodecs==2023.1.23 127 | imageio==2.33.1 128 | imagesize==1.4.1 129 | imbalanced-learn==0.11.0 130 | importlib-metadata==7.0.1 131 | importlib-resources==6.1.1 132 | incremental==21.3.0 133 | inflection==0.5.1 134 | iniconfig==1.1.1 135 | intake==0.6.8 136 | interface-meta==1.3.0 137 | interpolation==2.2.4 138 | intervaltree==3.1.0 139 | ipykernel==6.28.0 140 | ipython==8.15.0 141 | ipython-genutils==0.2.0 142 | ipywidgets==8.0.4 143 | isort==5.9.3 144 | itemadapter==0.3.0 145 | itemloaders==1.0.4 146 | itsdangerous==2.0.1 147 | jaraco.classes==3.2.1 148 | jdcal==1.4.1 149 | jedi==0.18.1 150 | jellyfish==1.0.1 151 | Jinja2==3.1.3 152 | jinja2-time==0.2.0 153 | jmespath==1.0.1 154 | joblib==1.2.0 155 | json5==0.9.6 156 | jsonpatch==1.32 157 | jsonpointer==2.1 158 | jsonschema==4.19.2 159 | jsonschema-specifications==2023.7.1 160 | jupyter==1.0.0 161 | jupyter_client==8.6.0 162 | jupyter-console==6.6.3 163 | jupyter_core==5.5.0 164 | jupyter-events==0.8.0 165 | jupyter-lsp==2.2.0 166 | jupyter_server==2.10.0 167 | jupyter_server_fileid==0.9.0 168 | jupyter-server-mathjax==0.2.6 169 | jupyter_server_terminals==0.4.4 170 | jupyter_server_ydoc==0.8.0 171 | jupyter-ydoc==0.2.4 172 | jupyterlab==4.0.11 173 | jupyterlab_git==0.50.0 174 | jupyterlab-pygments==0.1.2 175 | jupyterlab_server==2.25.1 176 | jupyterlab-widgets==3.0.9 177 | keyring==23.13.1 178 | kiwisolver==1.4.4 179 | lazy_loader==0.3 180 | lazy-object-proxy==1.6.0 181 | lckr_jupyterlab_variableinspector==3.1.0 182 | libarchive-c==2.9 183 | libmambapy==1.5.6 184 | linearmodels==5.1 185 | linkify-it-py==2.0.0 186 | llvmlite==0.42.0 187 | lmdb==1.4.1 188 | locket==1.0.0 189 | lxml==4.9.3 190 | lz4==4.3.2 191 | Markdown==3.4.1 192 | markdown-it-py==2.2.0 193 | MarkupSafe==2.1.3 194 | matplotlib==3.8.0 195 | matplotlib-inline==0.1.6 196 | mccabe==0.7.0 197 | mdit-py-plugins==0.3.0 198 | mdurl==0.1.0 199 | menuinst==2.0.2 200 | mistune==2.0.4 201 | mkl-fft==1.3.1 202 | mkl-random==1.2.2 203 | mkl-service==2.4.0 204 | more-itertools==10.1.0 205 | mpmath==1.3.0 206 | msgpack==1.0.3 207 | multidict==6.0.4 208 | multipledispatch==0.6.0 209 | munkres==1.1.4 210 | mypy==1.8.0 211 | mypy-extensions==1.0.0 212 | navigator-updater==0.4.0 213 | nbclassic==1.0.0 214 | nbclient==0.8.0 215 | nbconvert==7.10.0 216 | nbdime==4.0.1 217 | nbformat==5.9.2 218 | nest-asyncio==1.6.0 219 | networkx==3.1 220 | nltk==3.8.1 221 | nose==1.3.7 222 | notebook==7.0.8 223 | notebook_shim==0.2.3 224 | numba==0.59.0 225 | numexpr==2.8.7 226 | numpy==1.26.3 227 | numpydoc==1.5.0 228 | olefile==0.46 229 | openpyxl==3.0.10 230 | overrides==7.4.0 231 | packaging==21.3 232 | pandas==2.1.4 233 | pandocfilters==1.5.0 234 | panel==1.3.8 235 | param==2.0.2 236 | parsel==1.8.1 237 | parso==0.8.3 238 | partd==1.4.1 239 | pathlib==1.0.1 240 | pathspec==0.10.3 241 | patsy==0.5.3 242 | pep8==1.7.1 243 | pexpect==4.8.0 244 | pickleshare==0.7.5 245 | pillow==10.2.0 246 | pip==23.3.1 247 | pkce==1.0.3 248 | pkginfo==1.9.6 249 | platformdirs==3.10.0 250 | plotly==5.9.0 251 | pluggy==1.0.0 252 | ply==3.11 253 | pooch==1.7.0 254 | poyo==0.5.0 255 | prometheus-client==0.14.1 256 | prompt-toolkit==3.0.43 257 | Protego==0.1.16 258 | protobuf==3.20.3 259 | psutil==5.9.0 260 | ptyprocess==0.7.0 261 | pure-eval==0.2.2 262 | py==1.11.0 263 | py-cpuinfo==9.0.0 264 | pyarrow==14.0.2 265 | pyasn1==0.4.8 266 | pyasn1-modules==0.2.8 267 | pycodestyle==2.11.1 268 | pycosat==0.6.6 269 | pycparser==2.21 270 | pyct==0.5.0 271 | pycurl==7.45.2 272 | pydantic==1.10.12 273 | pydeck==0.7.1 274 | PyDispatcher==2.0.5 275 | pydocstyle==6.3.0 276 | pyerfa==2.0.0 277 | pyflakes==3.1.0 278 | Pygments==2.15.1 279 | pyhdfe==0.1.2 280 | PyJWT==2.4.0 281 | pylint==2.16.2 282 | pylint-venv==3.0.3 283 | pyls-spyder==0.4.0 284 | Pympler==0.9 285 | pyobjc-core==9.0 286 | pyobjc-framework-Cocoa==9.0 287 | pyobjc-framework-CoreServices==9.0 288 | pyobjc-framework-FSEvents==9.0 289 | pyodbc==5.0.1 290 | pyOpenSSL==24.0.0 291 | pyparsing==3.0.9 292 | PyQt5==5.15.10 293 | PyQt5-sip==12.13.0 294 | PyQtWebEngine==5.15.6 295 | pyrsistent==0.20.0 296 | PySocks==1.7.1 297 | pytest==7.4.0 298 | python-dateutil==2.8.2 299 | python-dotenv==0.21.0 300 | python-json-logger==2.0.7 301 | python-lsp-black==1.2.1 302 | python-lsp-jsonrpc==1.1.2 303 | python-lsp-server==1.9.0 304 | python-slugify==5.0.2 305 | python-snappy==0.6.1 306 | pytoolconfig==1.2.6 307 | pytz==2023.3.post1 308 | pyviz_comms==3.0.0 309 | pywavelets==1.5.0 310 | PyYAML==6.0.1 311 | pyzmq==25.1.2 312 | QDarkStyle==3.2.3 313 | qstylizer==0.2.2 314 | QtAwesome==1.2.2 315 | qtconsole==5.5.0 316 | QtPy==2.4.1 317 | quantecon==0.7.0 318 | queuelib==1.6.2 319 | referencing==0.30.2 320 | regex==2023.10.3 321 | requests==2.31.0 322 | requests-file==1.5.1 323 | requests-toolbelt==1.0.0 324 | rfc3339-validator==0.1.4 325 | rfc3986-validator==0.1.1 326 | rich==13.3.5 327 | rope==1.7.0 328 | rpds-py==0.10.6 329 | Rtree==1.0.1 330 | ruamel.yaml==0.17.21 331 | ruamel.yaml.clib==0.2.6 332 | ruamel-yaml-conda==0.17.21 333 | s3fs==2023.10.0 334 | s3transfer==0.7.0 335 | scikit-image==0.22.0 336 | scikit-learn==1.3.0 337 | scikit-learn-intelex==20230426.61523 338 | scipy==1.11.4 339 | Scrapy==2.8.0 340 | seaborn==0.12.2 341 | semver==2.13.0 342 | Send2Trash==1.8.2 343 | service-identity==18.1.0 344 | setuptools==68.2.2 345 | setuptools-scm==7.1.0 346 | sip==6.7.12 347 | six==1.16.0 348 | smart-open==5.2.1 349 | smmap==4.0.0 350 | sniffio==1.3.0 351 | snowballstemmer==2.2.0 352 | sortedcollections==2.1.0 353 | sortedcontainers==2.4.0 354 | soupsieve==2.5 355 | Sphinx==5.0.2 356 | sphinxcontrib-applehelp==1.0.2 357 | sphinxcontrib-devhelp==1.0.2 358 | sphinxcontrib-htmlhelp==2.0.0 359 | sphinxcontrib-jsmath==1.0.1 360 | sphinxcontrib-qthelp==1.0.3 361 | sphinxcontrib-serializinghtml==1.1.5 362 | spyder==5.5.0 363 | spyder-kernels==2.5.0 364 | SQLAlchemy==2.0.25 365 | stack-data==0.2.0 366 | stata-setup==0.1.3 367 | statsmodels==0.14.0 368 | streamlit==1.22.0 369 | sympy==1.12 370 | tables==3.9.2 371 | tabulate==0.9.0 372 | TBB==0.2 373 | tblib==1.7.0 374 | Tempita==0.5.3.dev0 375 | tenacity==8.2.2 376 | terminado==0.17.1 377 | testpath==0.6.0 378 | text-unidecode==1.3 379 | textdistance==4.2.1 380 | threadpoolctl==2.2.0 381 | three-merge==0.1.1 382 | tifffile==2023.4.12 383 | tinycss2==1.2.1 384 | tldextract==3.2.0 385 | tokenizers==0.13.3 386 | toml==0.10.2 387 | tomli==2.0.1 388 | tomlkit==0.11.1 389 | toolz==0.12.0 390 | torch==1.13.1 391 | tornado==6.3.3 392 | tqdm==4.65.0 393 | traitlets==5.7.1 394 | transformers==4.24.0 395 | Twisted==22.10.0 396 | typing_extensions==4.9.0 397 | tzdata==2023.3 398 | tzlocal==2.1 399 | uc-micro-py==1.0.1 400 | ujson==5.4.0 401 | Unidecode==1.2.0 402 | urllib3==1.26.18 403 | validators==0.18.2 404 | w3lib==1.21.0 405 | watchdog==2.1.6 406 | wcwidth==0.2.5 407 | webencodings==0.5.1 408 | websocket-client==0.58.0 409 | Werkzeug==2.2.3 410 | whatthepatch==1.0.2 411 | wheel==0.41.2 412 | widgetsnbextension==4.0.5 413 | wrapt==1.14.1 414 | wurlitzer==3.0.2 415 | xarray==2023.6.0 416 | xlrd==2.0.1 417 | XlsxWriter==3.1.1 418 | xlwings==0.29.1 419 | xyzservices==2022.9.0 420 | y-py==0.5.9 421 | yapf==0.40.1 422 | yarl==1.9.3 423 | ypy-websocket==0.8.2 424 | zict==3.0.0 425 | zipp==3.17.0 426 | zope.interface==5.4.0 427 | zstandard==0.19.0 428 | -------------------------------------------------------------------------------- /RANK/DSGE/Standard/mediumscale_ss.m: -------------------------------------------------------------------------------- 1 | %steady state values for mediumscale.mod 2 | 3 | load param_mediumscale; 4 | 5 | %Tech Shock 6 | A_ss = 1; 7 | 8 | %MEI Shock 9 | Z_ss = 1; 10 | 11 | %Inter Pref Shock 12 | nu_ss = 1; 13 | 14 | %gross inflation 15 | Pi_ss = 1; 16 | 17 | %price level 18 | P_ss = 1; 19 | 20 | %gross interest 21 | R_ss = Pi_ss/beta; 22 | 23 | %capital utilization (imposed) 24 | u_ss = 1; 25 | 26 | %multipliers are equal in SS. lambda_1_ss = lambda_2_ss from FOC 27 | %investment. So from FOC Capital 28 | rk_ss = 1/beta - (1-delta_0); 29 | 30 | %rental rate capital. From FOC capital utilization 31 | delta_1 = rk_ss - delta_2*(u_ss-1); 32 | 33 | %reset price. From price law of motion 34 | Pi_star_ss = ((Pi_ss^(1-epsilon_p) - theta_p*Pi_ss^(zeta_p*(1-epsilon_p)))/(1-theta_p))^(1/(1-epsilon_p)); 35 | 36 | %price dispersion. from price dispersion 37 | vp_ss = ((1-theta_p)*(Pi_star_ss/Pi_ss)^(-epsilon_p))/(1-theta_p*Pi_ss^(epsilon_p*(1-zeta_p))); 38 | 39 | %marginal cost. From x1/x2 ratio 40 | mc_ss = ((epsilon_p-1)/epsilon_p)*(Pi_star_ss/Pi_ss)*((1-theta_p*beta*Pi_ss^(epsilon_p*(1-zeta_p)))/(1-theta_p*beta*Pi_ss^((epsilon_p-1)*(zeta_p-1)))); 41 | 42 | %from capital services equation. k=k_hat in ss since u=1 43 | 44 | %capital labor ratio 45 | k_nd_ratio = ((alpha*mc_ss)/rk_ss)^(1/(1-alpha)); 46 | 47 | %real wage. Comes from plugging in capital/labor ratio into marginal cost 48 | %for rk. 49 | w_real_ss = (1-alpha)*mc_ss*k_nd_ratio^(alpha); 50 | 51 | %reset wage. from wage law of motion 52 | w_star_ss = ((w_real_ss^(1-epsilon_w)*(1-theta_w*Pi_ss^((epsilon_w-1)*(1-zeta_w))))/(1-theta_w))^(1/(1-epsilon_w)); 53 | 54 | 55 | %wage disperion 56 | vw_ss = ((1-theta_w)*(w_star_ss/w_real_ss)^(-epsilon_w*(1+eta)))/(1-theta_w*Pi_ss^(epsilon_w*(1+eta))*Pi_ss^(-zeta_w*epsilon_w*(1+eta))); 57 | 58 | %consumption labor ratio. comes from profit function when F is picked such 59 | %that profits are zero 60 | c_nd_ratio = (1-omega)*(w_real_ss+rk_ss*k_nd_ratio) - delta_0*k_nd_ratio; 61 | 62 | %steady state labor 63 | psi_ss = 19.18; 64 | Nd_ss = ((1/psi_ss)*((epsilon_w-1)/epsilon_w)*(1/c_nd_ratio)*((1-beta*b)/(1-b))*w_star_ss*((w_real_ss/w_star_ss)^(-epsilon_w*eta))* (((1-theta_w*beta*Pi_ss^(epsilon_w*(1+eta)*(1-zeta_w))))/(1-theta_w*beta*Pi_ss^((epsilon_w-1)*(1-zeta_w)))))^(1/(1+eta)); 65 | 66 | %fixed cost 67 | F = Nd_ss*(k_nd_ratio^(alpha)-(w_real_ss+rk_ss*k_nd_ratio)*vp_ss); 68 | 69 | %capital services 70 | K_hat_ss=k_nd_ratio*Nd_ss; 71 | %K_hat_ss=((alpha*mc_ss)/rk_ss)^(1/(1-alpha))*Nd_ss; equivalently 72 | 73 | %capital stock. from capital services equation 74 | K_ss = K_hat_ss/u_ss; 75 | 76 | %investment. from capital accumulation 77 | inv_ss = delta_0*K_ss; 78 | 79 | %output from resource constraint 80 | Y_ss = (A_ss*K_hat_ss^(alpha)*Nd_ss^(1-alpha)-F)/vp_ss; 81 | %Y_ss = (C_ss + inv_ss)/(1-omega); 82 | 83 | %government 84 | G_ss = omega*Y_ss; 85 | 86 | %consumption 87 | C_ss = Y_ss - inv_ss - G_ss; 88 | %C_ss = c_nd_ratio*Nd_ss; equivalently 89 | 90 | %lambda1. from FOC consumption 91 | lambda_1_ss = (1/C_ss)*((1-beta*b)/(1-b)); 92 | 93 | %lagrange multipliers. From FOC Investment 94 | lambda_2_ss = lambda_1_ss; 95 | 96 | %aggregate labor supply 97 | N_ss = vw_ss*Nd_ss; 98 | 99 | %output gap 100 | Xg_ss = alpha*log(u_ss); 101 | 102 | %price auxiliary eq 1 103 | X1_ss = (lambda_1_ss*mc_ss*Y_ss)/(1-theta_p*beta*Pi_ss^(epsilon_p*(1-zeta_p))); 104 | 105 | %price auxiliary eq 2 106 | X2_ss = (lambda_1_ss*Y_ss)/(1-theta_p*beta*Pi_ss^((epsilon_p-1)*(zeta_p-1))); 107 | 108 | %wage auxiliary eq 1 109 | H1_ss = (psi_ss*(w_real_ss/w_star_ss)^(epsilon_w*(1+eta))*Nd_ss^(1+eta))/(1-theta_w*beta*Pi_ss^(epsilon_w*(1+eta)*(1-zeta_w))); 110 | 111 | %wage auxiliary eq 2 112 | H2_ss = (lambda_1_ss*(w_real_ss/w_star_ss)^(epsilon_w)*Nd_ss)/(1-theta_w*beta*Pi_ss^((epsilon_w-1)*(1-zeta_w))); 113 | 114 | %ngdp 115 | NY_ss = P_ss*Y_ss; 116 | 117 | %welfare 118 | Welfare_ss = ((log(C_ss - b*C_ss) - psi_ss*vw_ss*(Nd_ss^(1+eta)/(1+eta))))/(1-beta); 119 | 120 | %logged variables 121 | log_A_ss = log(A_ss); 122 | log_Z_ss = log(Z_ss); 123 | log_G_ss = log(G_ss); 124 | log_psi_ss = log(psi_ss); 125 | 126 | log_nu_ss = log(nu_ss); 127 | pi_ss = log(Pi_ss); 128 | i_ss = log(R_ss); 129 | log_Y_ss = log(Y_ss); 130 | log_C_ss = log(C_ss); 131 | log_Nd_ss = log(Nd_ss); 132 | log_I_ss = log(inv_ss); 133 | log_w_ss = log(w_real_ss); 134 | 135 | 136 | dY_ss = 0; 137 | dC_ss = 0; 138 | dNd_ss = 0; 139 | dI_ss = 0; 140 | dw_ss = 0; 141 | dN_ss=0; 142 | 143 | 144 | 145 | -------------------------------------------------------------------------------- /RANK/DSGE/Standard/run_mediumscale.m: -------------------------------------------------------------------------------- 1 | close all 2 | clear 3 | 4 | %calibration from Sims NGDP priors. 5 | beta=0.995; %discount factor 6 | b=0.75; %habit persistence parameter 7 | delta_0 = 0.025; %depreciation rate 8 | delta_2 = 0.01; %capital utilization, quadratic term 9 | kappa=4; %capital adjustment cost parameter 10 | epsilon_w=11; %labor elasticity of substitution 11 | theta_w=0.75; %wage calvo parameter 12 | eta=2; %inverse frisch elasticity 13 | zeta_w=0; %wage indexation 14 | zeta_p=0; %price indexation 15 | alpha=1/3; %capital share 16 | theta_p=0.75; %calvo price parameter 17 | epsilon_p=11; %elasticity of substitution goods 18 | rho_g=0.95; %autocorrelation government consumption 19 | rho_r=0.8; %autocorrelation interest 20 | rho_a=0.95; %autocorrelation technology shock 21 | rho_z=0.8; %autocorrelation MEI 22 | rho_nu=0.8; %autocorrelation intertemporal preference 23 | rho_psi=0.8; %autocorrelation intratemporal preference 24 | omega=0.2; %steady state government consumption 25 | 26 | phi_pi=1.5; %inflation feedback 27 | phi_y=0.25; %output growth feedback 28 | phi_x=0.05; %output gap feedback 29 | 30 | save param_mediumscale beta b delta_0 delta_2 kappa epsilon_w theta_w eta zeta_w zeta_p alpha theta_p epsilon_p rho_g rho_r rho_a rho_z rho_nu rho_psi omega phi_pi phi_y phi_x 31 | 32 | dynare mediumscale noclearall nolog 33 | 34 | close all 35 | 36 | 37 | figure(1) 38 | subplot(3,2,1) 39 | plot(100*Y_eA/Y_ss,'k','Linewidth',1.5) 40 | title('Output') 41 | ylabel('% dev.from s.s.') 42 | %hold on 43 | %plot(100*yf_eA/yf_ss,'--k','Linewidth',1.5) 44 | %hold off 45 | grid on 46 | 47 | subplot(3,2,2) 48 | plot(400*(Pi_eA+Pi_ss-1),'k','Linewidth',1.5) 49 | title('Inflation (annualized)') 50 | ylabel('Annualized Level, PPt') 51 | %plot(400*Pi_eNU/Pi_ss,'k','Linewidth',1.5) 52 | %title('Inflation (annualized)') 53 | %ylabel('% dev.from s.s.') 54 | grid on 55 | 56 | 57 | subplot(3,2,3) 58 | plot(4*100*(R_eA+R_ss-1),'k','Linewidth',1.5) 59 | title('Interest Rate (annualized)') 60 | ylabel('Level in % pt.') 61 | grid on 62 | 63 | subplot(3,2,4) 64 | plot(100*(C_eA/C_ss),'k','Linewidth',1.5) 65 | title('Consumption') 66 | ylabel('% dev.from s.s.') 67 | grid on 68 | 69 | subplot(3,2,5) 70 | plot(Xg_eA,'k','Linewidth',1.5) 71 | title('Output Gap') 72 | ylabel('abs. dev.from s.s.') 73 | grid on 74 | 75 | subplot(3,2,6) 76 | plot(log_A_eA,'k','Linewidth',1.5) 77 | title('Technology Shock') 78 | ylabel('Level') 79 | grid on 80 | 81 | -------------------------------------------------------------------------------- /RANK/DSGE/ZLB/medium_scale_dsge_2016.pdf: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/hessjacob/Quantitative-Macro-Models/f548ac496dc0d440a5ecb251e01b84e4c0580641/RANK/DSGE/ZLB/medium_scale_dsge_2016.pdf -------------------------------------------------------------------------------- /RANK/DSGE/ZLB/mediumscale_ss_zlb.m: -------------------------------------------------------------------------------- 1 | %steady state values for mediumscale.mod 2 | 3 | load param_mediumscale; 4 | 5 | %Tech Shock 6 | A_ss = 1; 7 | 8 | %MEI Shock 9 | Z_ss = 1; 10 | 11 | %Inter Pref Shock 12 | nu_ss = 1; 13 | 14 | %gross inflation 15 | Pi_ss = 1; 16 | 17 | %price level 18 | P_ss = 1; 19 | 20 | %gross interest 21 | R_ss = Pi_ss/beta; 22 | 23 | %capital utilization (imposed) 24 | u_ss = 1; 25 | 26 | %multipliers are equal in SS. lambda_1_ss = lambda_2_ss from FOC 27 | %investment. So from FOC Capital 28 | rk_ss = 1/beta - (1-delta_0); 29 | 30 | %rental rate capital. From FOC capital utilization 31 | delta_1 = rk_ss - delta_2*(u_ss-1); 32 | 33 | %reset price. From price law of motion 34 | Pi_star_ss = ((Pi_ss^(1-epsilon_p) - theta_p*Pi_ss^(zeta_p*(1-epsilon_p)))/(1-theta_p))^(1/(1-epsilon_p)); 35 | 36 | %price dispersion. from price dispersion 37 | vp_ss = ((1-theta_p)*(Pi_star_ss/Pi_ss)^(-epsilon_p))/(1-theta_p*Pi_ss^(epsilon_p*(1-zeta_p))); 38 | 39 | %marginal cost. From x1/x2 ratio 40 | mc_ss = ((epsilon_p-1)/epsilon_p)*(Pi_star_ss/Pi_ss)*((1-theta_p*beta*Pi_ss^(epsilon_p*(1-zeta_p)))/(1-theta_p*beta*Pi_ss^((epsilon_p-1)*(zeta_p-1)))); 41 | 42 | %from capital services equation. k=k_hat in ss since u=1 43 | 44 | %capital labor ratio 45 | k_nd_ratio = ((alpha*mc_ss)/rk_ss)^(1/(1-alpha)); 46 | 47 | %real wage. Comes from plugging in capital/labor ratio into marginal cost 48 | %for rk. 49 | w_real_ss = (1-alpha)*mc_ss*k_nd_ratio^(alpha); 50 | 51 | %reset wage. from wage law of motion 52 | w_star_ss = ((w_real_ss^(1-epsilon_w)*(1-theta_w*Pi_ss^((epsilon_w-1)*(1-zeta_w))))/(1-theta_w))^(1/(1-epsilon_w)); 53 | 54 | 55 | %wage disperion 56 | vw_ss = ((1-theta_w)*(w_star_ss/w_real_ss)^(-epsilon_w*(1+eta)))/(1-theta_w*Pi_ss^(epsilon_w*(1+eta))*Pi_ss^(-zeta_w*epsilon_w*(1+eta))); 57 | 58 | %consumption labor ratio. comes from profit function when F is picked such 59 | %that profits are zero 60 | c_nd_ratio = (1-omega)*(w_real_ss+rk_ss*k_nd_ratio) - delta_0*k_nd_ratio; 61 | 62 | %steady state labor 63 | psi_ss = 19.18; 64 | Nd_ss = ((1/psi_ss)*((epsilon_w-1)/epsilon_w)*(1/c_nd_ratio)*((1-beta*b)/(1-b))*w_star_ss*((w_real_ss/w_star_ss)^(-epsilon_w*eta))* (((1-theta_w*beta*Pi_ss^(epsilon_w*(1+eta)*(1-zeta_w))))/(1-theta_w*beta*Pi_ss^((epsilon_w-1)*(1-zeta_w)))))^(1/(1+eta)); 65 | 66 | %fixed cost 67 | F = Nd_ss*(k_nd_ratio^(alpha)-(w_real_ss+rk_ss*k_nd_ratio)*vp_ss); 68 | 69 | %capital services 70 | K_hat_ss=k_nd_ratio*Nd_ss; 71 | %K_hat_ss=((alpha*mc_ss)/rk_ss)^(1/(1-alpha))*Nd_ss; equivalently 72 | 73 | %capital stock. from capital services equation 74 | K_ss = K_hat_ss/u_ss; 75 | 76 | %investment. from capital accumulation 77 | inv_ss = delta_0*K_ss; 78 | 79 | %output from resource constraint 80 | Y_ss = (A_ss*K_hat_ss^(alpha)*Nd_ss^(1-alpha)-F)/vp_ss; 81 | %Y_ss = (C_ss + inv_ss)/(1-omega); 82 | 83 | %government 84 | G_ss = omega*Y_ss; 85 | 86 | %consumption 87 | C_ss = Y_ss - inv_ss - G_ss; 88 | %C_ss = c_nd_ratio*Nd_ss; equivalently 89 | 90 | %lambda1. from FOC consumption 91 | lambda_1_ss = (1/C_ss)*((1-beta*b)/(1-b)); 92 | 93 | %lagrange multipliers. From FOC Investment 94 | lambda_2_ss = lambda_1_ss; 95 | 96 | %aggregate labor supply 97 | N_ss = vw_ss*Nd_ss; 98 | 99 | %output gap 100 | Xg_ss = alpha*log(u_ss); 101 | 102 | %price auxiliary eq 1 103 | X1_ss = (lambda_1_ss*mc_ss*Y_ss)/(1-theta_p*beta*Pi_ss^(epsilon_p*(1-zeta_p))); 104 | 105 | %price auxiliary eq 2 106 | X2_ss = (lambda_1_ss*Y_ss)/(1-theta_p*beta*Pi_ss^((epsilon_p-1)*(zeta_p-1))); 107 | 108 | %wage auxiliary eq 1 109 | H1_ss = (psi_ss*(w_real_ss/w_star_ss)^(epsilon_w*(1+eta))*Nd_ss^(1+eta))/(1-theta_w*beta*Pi_ss^(epsilon_w*(1+eta)*(1-zeta_w))); 110 | 111 | %wage auxiliary eq 2 112 | H2_ss = (lambda_1_ss*(w_real_ss/w_star_ss)^(epsilon_w)*Nd_ss)/(1-theta_w*beta*Pi_ss^((epsilon_w-1)*(1-zeta_w))); 113 | 114 | %ngdp 115 | NY_ss = P_ss*Y_ss; 116 | 117 | %welfare 118 | Welfare_ss = ((log(C_ss - b*C_ss) - psi_ss*vw_ss*(Nd_ss^(1+eta)/(1+eta))))/(1-beta); 119 | 120 | %logged variables 121 | log_A_ss = log(A_ss); 122 | log_Z_ss = log(Z_ss); 123 | log_G_ss = log(G_ss); 124 | log_psi_ss = log(psi_ss); 125 | 126 | log_nu_ss = log(nu_ss); 127 | pi_ss = log(Pi_ss); 128 | i_ss = log(R_ss); 129 | log_Y_ss = log(Y_ss); 130 | log_C_ss = log(C_ss); 131 | log_Nd_ss = log(Nd_ss); 132 | log_I_ss = log(inv_ss); 133 | log_w_ss = log(w_real_ss); 134 | 135 | 136 | dY_ss = 0; 137 | dC_ss = 0; 138 | dNd_ss = 0; 139 | dI_ss = 0; 140 | dw_ss = 0; 141 | 142 | 143 | 144 | -------------------------------------------------------------------------------- /RANK/DSGE/ZLB/run_mediumscale_zlb.m: -------------------------------------------------------------------------------- 1 | close all 2 | clear 3 | 4 | %calibration from Sims NGDP priors. 5 | beta=0.995; %discount factor 6 | b=0.7; %habit persistence parameter 7 | delta_0 = 0.025; %depreciation rate 8 | delta_2 = 0.01; %capital utilization, quadratic term 9 | kappa=4; %capital adjustment cost parameter 10 | epsilon_w=11; %labor elasticity of substitution 11 | theta_w=0.75; %wage calvo parameter 12 | eta=2; %inverse frisch elasticity 13 | zeta_w=0; %wage indexation 14 | zeta_p=0; %price indexation 15 | alpha=1/3; %capital share 16 | theta_p=0.75; %calvo price parameter 17 | epsilon_p=11; %elasticity of substitution goods 18 | rho_g=0.95; %autocorrelation government consumption 19 | rho_r=0.8; %autocorrelation interest 20 | rho_a=0.95; %autocorrelation technology shock 21 | rho_z=0.8; %autocorrelation MEI 22 | rho_nu=0.8; %autocorrelation intertemporal preference 23 | rho_psi=0.8; %autocorrelation intratemporal preference 24 | omega=0.2; %steady state government consumption 25 | 26 | phi_pi=1.5; %inflation feedback 27 | phi_y=0.25; %output growth feedback 28 | phi_x=0.05; %output gap feedback 29 | Rre=1; %gross interest rate lower bound 30 | 31 | save param_mediumscale beta b delta_0 delta_2 kappa epsilon_w theta_w eta zeta_w zeta_p alpha theta_p epsilon_p rho_g rho_r rho_a rho_z rho_nu rho_psi omega phi_pi phi_y phi_x Rre 32 | 33 | mediumscale_ss_zlb 34 | 35 | global M_ oo_ 36 | 37 | addpath('/Applications/Dynare/4.5.7/matlab/occbin_20140630/toolkit_files') 38 | nperiods=50; 39 | maxiter=50; 40 | tol0 = 1e-8; 41 | 42 | modnam = 'mediumscale_baseline'; 43 | modnamstar = 'mediumscale_zlb nostrict'; %add nostrict here because eM is part of the the system but not in any equation in the zlb mod. This skips over the warning. 44 | 45 | constraint = 'R