'vars_lvl': vars_lvl,
'dist_last': sims[-1, ]
}
# %% [markdown]
# We now configure and solve a buffer-stock agent with a default parametrization.
# %% Create and simulate agent tags=[]
# Create and solve agent
popn = IndShockConsumerType(**init_idiosyncratic_shocks)
# Modify default parameters
popn.T_sim = max(sample_periods_lvl) + 1
popn.AgentCount = max_agents
popn.track_vars = ['mNrm', 'cNrm', 'pLvl']
popn.LivPrb = [1.0]
popn.cycles = 0
# Solve (but do not yet simulate)
popn.solve()
# %% [markdown]
# Under the basic simulation strategy, we have to de-normalize market resources and consumption multiplying them by permanent income. Only then we construct our statistics of interest.
#
# Note that our time-sampling strategy requires that, after enough time has passed, the economy settles on a stable distribution of its agents across states. How can we know this will be the case? [Szeidl (2013)](http://www.personal.ceu.hu/staff/Adam_Szeidl/papers/invariant.pdf) and [Harmenberg (2021)](https://www.sciencedirect.com/science/article/pii/S0165188921001202?via%3Dihub) provide conditions that can give us some reassurance.$\newcommand{\Rfree}{\mathsf{R}}$
#
# 1. [Szeidl (2013)](http://www.personal.ceu.hu/staff/Adam_Szeidl/papers/invariant.pdf) shows that if $$\log \left[\frac{(\Rfree\beta)^{1/\rho}}{\PermGroFac}
# \right] < \Ex[\log \PermShk],$$ then there is a stable invariant distribution of normalized market resources $\mNrm$.
# 2. [Harmenberg (2021)](https://www.sciencedirect.com/science/article/pii/S0165188921001202?via%3Dihub) repurposes the Szeidl proof to argue that if the same condition is satisfied when the expectation is taken with respect to the permanent-income-neutral measure ($\pShkNeutDstn$), then there is a stable invariant permanent-income-weighted distribution ($\mWgtDstnMarg$)
from builtins import range
import numpy as np
from copy import deepcopy
# Now, bring in what we need from the cstwMPC parameters
import HARK.cstwMPC.SetupParamsCSTW as cstwParams
from HARK.utilities import approxUniform
## Import the HARK ConsumerType we want
## Here, we bring in an agent making a consumption/savings decision every period, subject
## to transitory and permanent income shocks.
from HARK.ConsumptionSaving.ConsIndShockModel import IndShockConsumerType
# Now initialize a baseline consumer type, using default parameters from infinite horizon cstwMPC
BaselineType = IndShockConsumerType(**cstwParams.init_infinite)
BaselineType.AgentCount = 10000 # Assign the baseline consumer type to have many agents in simulation
####################################################################################################
####################################################################################################
"""
Now, add in ex-ante heterogeneity in consumers' discount factors
"""
# The cstwMPC parameters do not define a discount factor, since there is ex-ante heterogeneity
# in the discount factor. To prepare to create this ex-ante heterogeneity, first create
# the desired number of consumer types
num_consumer_types = 7 # declare the number of types we want
ConsumerTypes = [] # initialize an empty list
for nn in range(num_consumer_types):
# Now create the types, and append them to the list ConsumerTypes
