Merge pull request #200 from alanlujan91/cstwmpc

use AgentPopulation

notebooks/Alternative-Combos-Of-Parameter-Values.py

@@ -9,7 +9,7 @@
 #       extension: .py
 #       format_name: percent
 #       format_version: '1.3'
-#       jupytext_version: 1.14.4
+#       jupytext_version: 1.14.5
 #   kernelspec:
 #     display_name: Python 3 (ipykernel)
 #     language: python

notebooks/ChangeLiqConstr.py

@@ -9,7 +9,7 @@
 #       extension: .py
 #       format_name: percent
 #       format_version: '1.3'
-#       jupytext_version: 1.14.4
+#       jupytext_version: 1.14.5
 #   kernelspec:
 #     display_name: Python 3 (ipykernel)
 #     language: python

notebooks/Chinese-Growth.py

@@ -8,7 +8,7 @@
 #       extension: .py
 #       format_name: percent
 #       format_version: '1.3'
-#       jupytext_version: 1.14.4
+#       jupytext_version: 1.14.5
 #   kernelspec:
 #     display_name: Python 3
 #     language: python

notebooks/DCEGM-Upper-Envelope.py

@@ -10,7 +10,7 @@
 #       extension: .py
 #       format_name: percent
 #       format_version: '1.3'
-#       jupytext_version: 1.14.4
+#       jupytext_version: 1.14.5
 #   kernelspec:
 #     display_name: Python 3 (ipykernel)
 #     language: python

notebooks/DiamondOLG.py

@@ -7,7 +7,7 @@
 #       extension: .py
 #       format_name: percent
 #       format_version: '1.3'
-#       jupytext_version: 1.14.4
+#       jupytext_version: 1.14.5
 #   kernelspec:
 #     display_name: Python 3 (ipykernel)
 #     language: python
@@ -1444,7 +1444,7 @@
 # %% [markdown]
 # ### Convergence of OLG Economy to Steady State
 
-# %% tags=[]
+# %%
 # Some initial setup
 from HARK.ConsumptionSaving.ConsIndShockModel import (
     PerfForesightConsumerType,
@@ -1472,7 +1472,7 @@
 years_per_gen = 30
 
 
-# %% jupyter={"source_hidden": true} tags=[]
+# %% jupyter={"source_hidden": true}
 # Define a function that plots something given some inputs
 def plot1(Epsilon, DiscFac, PopGrowth, YearsPerGeneration, Initialk):
     """Inputs:
@@ -1568,7 +1568,7 @@ def plot1(Epsilon, DiscFac, PopGrowth, YearsPerGeneration, Initialk):
     return None
 
 
-# %% jupyter={"source_hidden": true} tags=[]
+# %% jupyter={"source_hidden": true}
 # Define some widgets to control the plot
 
 # Define a slider for Epsilon
@@ -1619,7 +1619,7 @@ def plot1(Epsilon, DiscFac, PopGrowth, YearsPerGeneration, Initialk):
     style={"description_width": "initial"},
 )
 
-# %% tags=[]
+# %%
 # Make the widget
 interact(
     plot1,
@@ -1885,7 +1885,7 @@ def plot3(Epsilon, DiscFac, z, Rfree, YearsPerGeneration):
 )
 
 
-# %% tags=[]
+# %%
 # Make the widget
 interact(
     plot3,

notebooks/FisherTwoPeriod.py

@@ -7,7 +7,7 @@
 #       extension: .py
 #       format_name: percent
 #       format_version: '1.3'
-#       jupytext_version: 1.14.4
+#       jupytext_version: 1.14.5
 #   kernelspec:
 #     display_name: Python 3
 #     language: python

notebooks/Gentle-Intro-To-HARK-Buffer-Stock-Model.py

@@ -8,7 +8,7 @@
 #       extension: .py
 #       format_name: percent
 #       format_version: '1.3'
-#       jupytext_version: 1.14.4
+#       jupytext_version: 1.14.5
 #   kernelspec:
 #     display_name: Python 3 (ipykernel)
 #     language: python

notebooks/Gentle-Intro-To-HARK-PerfForesightCRRA.py

@@ -8,7 +8,7 @@
 #       extension: .py
 #       format_name: percent
 #       format_version: '1.3'
-#       jupytext_version: 1.14.4
+#       jupytext_version: 1.14.5
 #   kernelspec:
 #     display_name: Python 3 (ipykernel)
 #     language: python

notebooks/Harmenberg-Aggregation.ipynb

@@ -55,7 +55,8 @@
    "cell_type": "markdown",
    "id": "d940f86a",
    "metadata": {
-    "incorrectly_encoded_metadata": "jp-MarkdownHeadingCollapsed=true tags=[] jp-MarkdownHeadingCollapsed=true"
+    "jp-MarkdownHeadingCollapsed": true,
+    "tags": []
    },
    "source": [
     "# Description of the problem\n",
@@ -865,7 +866,7 @@
  ],
  "metadata": {
   "jupytext": {
-   "cell_metadata_filter": "ExecuteTime,collapsed,title,code_folding,tags,incorrectly_encoded_metadata,-autoscroll",
+   "cell_metadata_filter": "ExecuteTime,collapsed,title,code_folding,tags,incorrectly_encoded_metadata,jp-MarkdownHeadingCollapsed,-autoscroll",
    "encoding": "# -*- coding: utf-8 -*-",
    "formats": "ipynb,py:percent",
    "notebook_metadata_filter": "all,-widgets,-varInspector"

notebooks/Harmenberg-Aggregation.py

@@ -2,14 +2,14 @@
 # ---
 # jupyter:
 #   jupytext:
-#     cell_metadata_filter: ExecuteTime,collapsed,title,code_folding,tags,incorrectly_encoded_metadata,-autoscroll
+#     cell_metadata_filter: ExecuteTime,collapsed,title,code_folding,tags,incorrectly_encoded_metadata,jp-MarkdownHeadingCollapsed,-autoscroll
 #     formats: ipynb,py:percent
 #     notebook_metadata_filter: all,-widgets,-varInspector
 #     text_representation:
 #       extension: .py
 #       format_name: percent
 #       format_version: '1.3'
-#       jupytext_version: 1.14.4
+#       jupytext_version: 1.14.5
 #   kernelspec:
 #     display_name: Python 3 (ipykernel)
 #     language: python
@@ -36,7 +36,7 @@
 # %% [markdown]
 # `# Set Up the Computational Environment: (in JupyterLab, click the dots)`
 
-# %% code_folding=[0] tags=[]
+# %% code_folding=[0]
 # Preliminaries
 import numpy as np
 import pandas as pd
@@ -49,7 +49,7 @@
     init_idiosyncratic_shocks,
 )
 
-# %% [markdown] jp-MarkdownHeadingCollapsed=true tags=[] jp-MarkdownHeadingCollapsed=true
+# %% [markdown] jp-MarkdownHeadingCollapsed=true
 # # Description of the problem
 #
 # $\newcommand{\pLvl}{\mathbf{p}}$
@@ -116,7 +116,7 @@
 #
 # This notebook briefly describes Harmenberg's method and demonstrates its implementation in the HARK toolkit.
 
-# %% [markdown] tags=[]
+# %% [markdown]
 # # Description of the method
 #
 # To illustrate Harmenberg's idea, consider a [buffer stock saving](https://econ-ark.github.io/BufferStockTheory) model in which:
@@ -188,7 +188,7 @@
 # %% [markdown]
 # `# Implementation in HARK:`
 
-# %% [markdown] code_folding=[] tags=[]
+# %% [markdown] code_folding=[]
 # #### Farther down in the notebook, code like this solves the standard model:
 #
 # ```python
@@ -212,7 +212,7 @@
 # popn.simulate()
 # ```
 
-# %% [markdown] code_folding=[] tags=[]
+# %% [markdown] code_folding=[]
 # #### Later, code like this simulates using the permanent-income-neutral measure
 # ```python
 # # Harmenberg permanent-income-neutral simulation
@@ -230,7 +230,7 @@
 # ntrl.simulate()
 # ```
 
-# %% [markdown] tags=[]
+# %% [markdown]
 # All we had to do differently to simulate using the permanent-income-neutral measure was to set the agent's property `neutral_measure=True`.
 #
 # This is implemented when the function `update_income_process` re-constructs the agent's income process. The specific lines that achieve the change of measure in HARK are in [this link](https://github.com/econ-ark/HARK/blob/760df611a6ec2ff147d00b7d866dbab6fc4e18a1/HARK/ConsumptionSaving/ConsIndShockModel.py#L2734-L2735), or reproduced here:
@@ -289,7 +289,7 @@
 # %% [markdown]
 # `# Setup computational environment:`
 
-# %% Experiment setup tags=[]
+# %% Experiment setup
 # How long to run the economies without sampling? T_0
 # Because we start the population at mBalLvl which turns out to be close
 # to MBalLvl so we don't need a long burn in period
@@ -319,7 +319,7 @@
 # %% [markdown]
 # `# Define tool to calculate summary statistics:`
 
-# %% Define function to get our stats of interest code_folding=[0] tags=[]
+# %% Define function to get our stats of interest code_folding=[0]
 # Now create a function that takes HARK's simulation output
 # and computes all the summary statistics we need
 
@@ -351,7 +351,7 @@ def sumstats(sims, sample_periods):
 # %% [markdown]
 # We now configure and solve a buffer-stock agent with a default parametrization.
 
-# %% Create and simulate agent tags=[]
+# %% Create and simulate agent
 # Create and solve agent
 
 popn = IndShockConsumerType(**init_idiosyncratic_shocks)
@@ -377,7 +377,7 @@ def sumstats(sims, sample_periods):
 #
 # We now check both conditions with our parametrization.
 
-# %% tags=[]
+# %%
 # Two versions for different HARK versions
 try:  # This works with HARK 2.0 pre-alpha
     Bilt = popn.solution[0].Bilt
@@ -413,7 +413,7 @@ def sumstats(sims, sample_periods):
 #
 # First, we simulate using the traditional approach.
 
-# %% tags=[]
+# %%
 # Base simulation
 
 # Start assets at m balanced growth (levels) point
@@ -436,7 +436,7 @@ def sumstats(sims, sample_periods):
 # %% [markdown]
 # Update and simulate using Harmenberg's strategy. This time, not multiplying by permanent income.
 
-# %% tags=[]
+# %%
 # Harmenberg permanent income neutral simulation
 
 # Start by duplicating the previous setup
@@ -462,7 +462,7 @@ def sumstats(sims, sample_periods):
 #
 # The plots below show the (logs of) the estimated variances for the two methods as a function of the (logs of) the number of agents.
 
-# %% code_folding=[0] tags=[]
+# %% code_folding=[0]
 # Plots
 
 # Construct aggregate levels and growth rates
@@ -531,7 +531,7 @@ def sumstats(sims, sample_periods):
 plt.show()
 
 
-# %% [markdown] tags=[]
+# %% [markdown]
 # # Harmenberg's Method Produces Large Gains in Efficiency
 #
 # The number of agents required to achieve a given variance is revealed by choosing that variance and then finding the points on the horizontal axis that correspond to the two methods.

notebooks/IncExpectationExample.py

@@ -9,7 +9,7 @@
 #       extension: .py
 #       format_name: percent
 #       format_version: '1.3'
-#       jupytext_version: 1.14.4
+#       jupytext_version: 1.14.5
 #   kernelspec:
 #     display_name: Python 3 (ipykernel)
 #     language: python

notebooks/KeynesFriedmanModigliani.py

@@ -9,7 +9,7 @@
 #       extension: .py
 #       format_name: percent
 #       format_version: '1.3'
-#       jupytext_version: 1.14.4
+#       jupytext_version: 1.14.5
 #   kernelspec:
 #     display_name: Python 3
 #     language: python

notebooks/LC-Model-Expected-Vs-Realized-Income-Growth.py

@@ -8,7 +8,7 @@
 #       extension: .py
 #       format_name: percent
 #       format_version: '1.3'
-#       jupytext_version: 1.14.4
+#       jupytext_version: 1.14.5
 #   kernelspec:
 #     display_name: Python 3 (ipykernel)
 #     language: python
@@ -25,15 +25,15 @@
 #     version: 3.10.8
 # ---
 
-# %% [markdown] tags=[]
+# %% [markdown]
 # # Expectated vs Realized Income Growth in A Standard Life Cycle Model
 
 # %% [markdown]
 # This notebook uses the income process in [Cocco, Gomes & Maenhout (2005)](https://academic.oup.com/rfs/article/18/2/491/1599892?login=true) to demonstrate that estimates of a regression of expected income changes on realized income changes are sensitive to the size of transitory shocks.
 #
 # We first load some tools from the [HARK toolkit](https://github.com/econ-ark/HARK).
 
-# %% jupyter={"source_hidden": true} tags=[]
+# %% jupyter={"source_hidden": true}
 import statsmodels.api as sm
 from linearmodels.panel.model import PanelOLS
 from HARK.distribution import calc_expectation
@@ -52,10 +52,10 @@
 import pandas as pd
 from copy import copy
 
-# %% [markdown] tags=[]
+# %% [markdown]
 # We now create a population of agents with the income process of [Cocco, Gomes & Maenhout (2005)](https://academic.oup.com/rfs/article/18/2/491/1599892?login=true), which is implemented as a default calibration in the toolkit.
 
-# %% Alter calibration jupyter={"source_hidden": true} tags=[]
+# %% Alter calibration jupyter={"source_hidden": true}
 birth_age = 21
 death_age = 66
 adjust_infl_to = 1992
@@ -104,7 +104,7 @@
 # %% Create and solve agent [markdown]
 # We simulate a population of agents
 
-# %% Simulation tags=[]
+# %% Simulation
 # %%capture
 # Run the simulations
 Agent.initialize_sim()
@@ -156,7 +156,7 @@
 # \end{equation}
 #
 
-# %% Compute expectations jupyter={"source_hidden": true} tags=[]
+# %% Compute expectations jupyter={"source_hidden": true}
 exp = [
     calc_expectation(Agent.IncShkDstn[i], func=lambda x: x[0] * x[1])
     for i in range(Agent.T_cycle)
@@ -197,7 +197,7 @@
 # \end{equation*}
 # We now estimate an analogous regression in our simulated population.
 
-# %% jupyter={"source_hidden": true} tags=[]
+# %% jupyter={"source_hidden": true}
 Data = Data.set_index(["id", "Age"])
 
 # Create the variables they actually use
@@ -218,7 +218,7 @@
 #
 # However, with less volatile transitory shocks, the regression coefficient would be positive. We demonstrate this by shutting off transitory shocks, simulating another population of agents, and re-running the regression.
 
-# %% tags=[]
+# %%
 # %%capture
 params_no_transitory = copy(params)
 params_no_transitory.update({"TranShkStd": [0.0] * len(params["TranShkStd"])})
@@ -231,7 +231,7 @@
 Agent_nt.simulate()
 
 
-# %% jupyter={"source_hidden": true} tags=[]
+# %% jupyter={"source_hidden": true}
 exp = [
     calc_expectation(Agent_nt.IncShkDstn[i], func=lambda x: x[0] * x[1])
     for i in range(Agent_nt.T_cycle)
@@ -266,7 +266,7 @@
 
 Data["Y_change"] = Data.groupby("id")["Y"].diff(1)
 
-# %% jupyter={"source_hidden": true} tags=[]
+# %% jupyter={"source_hidden": true}
 # Create variables
 Data["ExpBin"] = 0
 Data.loc[Data["ExpIncChange"] > 0, "ExpBin"] = 1

notebooks/LifeCycleModelTheoryVsData.py

@@ -9,7 +9,7 @@
 #       extension: .py
 #       format_name: percent
 #       format_version: '1.3'
-#       jupytext_version: 1.14.4
+#       jupytext_version: 1.14.5
 #   kernelspec:
 #     display_name: Python 3 (ipykernel)
 #     language: python