###############################################################################
# -----------------------------------------------------------------------------
# ----- Define additional parameters for the persistent shocks model ----------
# -----------------------------------------------------------------------------
pLvlPctiles = np.concatenate((
[0.001, 0.005, 0.01, 0.03],
np.linspace(0.05, 0.95, num=19),
[0.97, 0.99, 0.995, 0.999],
))
PrstIncCorr = 0.98 # Serial correlation coefficient for permanent income
# Make a dictionary for the "explicit permanent income" idiosyncratic shocks model
init_explicit_perm_inc = init_idiosyncratic_shocks.copy()
init_explicit_perm_inc["pLvlPctiles"] = pLvlPctiles
init_explicit_perm_inc["PermGroFac"] = [
1.0
] # long run permanent income growth doesn't work yet
init_explicit_perm_inc["aXtraMax"] = 30
init_explicit_perm_inc["aXtraExtra"] = [0.005, 0.01]
class GenIncProcessConsumerType(IndShockConsumerType):
"""
A consumer type with idiosyncratic shocks to persistent and transitory income.
His problem is defined by a sequence of income distributions, survival prob-
abilities, and persistent income growth functions, as well as time invariant
values for risk aversion, discount factor, the interest rate, the grid of
end-of-period assets, and an artificial borrowing constraint.
def getControls(self):
'''
Calculates consumption for the representative agent using the consumption functions.
Parameters
----------
None
Returns
-------
None
'''
t = self.t_cycle[0]
i = self.MrkvNow[0]
self.cNrmNow = self.solution[t].cFunc[i](self.mNrmNow)
# Define the default dictionary for a representative agent type
init_rep_agent = init_idiosyncratic_shocks.copy()
init_rep_agent["DeprFac"] = 0.05
init_rep_agent["CapShare"] = 0.36
init_rep_agent["UnempPrb"] = 0.0
init_rep_agent["LivPrb"] = [1.0]
# Define the default dictionary for a markov representative agent type
init_markov_rep_agent = init_rep_agent.copy()
init_markov_rep_agent["PermGroFac"] = [[0.97, 1.03]]
init_markov_rep_agent["MrkvArray"] = np.array([[0.99, 0.01], [0.01, 0.99]])
init_markov_rep_agent["MrkvNow"] = 0
vFuncFxd_now = None
# Create and return this period's solution
return PortfolioSolution(cFuncAdj=cFuncAdj_now,
ShareFuncAdj=ShareFuncAdj_now,
vPfuncAdj=vPfuncAdj_now,
vFuncAdj=vFuncAdj_now,
cFuncFxd=cFuncFxd_now,
ShareFuncFxd=ShareFuncFxd_now,
dvdmFuncFxd=dvdmFuncFxd_now,
dvdsFuncFxd=dvdsFuncFxd_now,
vFuncFxd=vFuncFxd_now)
# Make a dictionary to specify a portfolio choice consumer type
init_portfolio = init_idiosyncratic_shocks.copy()
init_portfolio['RiskyAvg'] = 1.08 # Average return of the risky asset
init_portfolio['RiskyStd'] = 0.20 # Standard deviation of (log) risky returns
init_portfolio[
'RiskyCount'] = 5 # Number of integration nodes to use in approximation of risky returns
init_portfolio[
'ShareCount'] = 25 # Number of discrete points in the risky share approximation
init_portfolio[
'AdjustPrb'] = 1.0 # Probability that the agent can adjust their risky portfolio share each period
init_portfolio[
'DiscreteShareBool'] = False # Flag for whether to optimize risky share on a discrete grid only
# Adjust some of the existing parameters in the dictionary
init_portfolio['aXtraMax'] = 100 # Make the grid of assets go much higher...
init_portfolio['aXtraCount'] = 200 # ...and include many more gridpoints...
init_portfolio[
----------
None
Returns
-------
None
"""
IndShockConsumerType.get_shocks(self)
self.get_Risky()
self.get_Adjust()
# %% Initial parameter sets
# %% Base risky asset dictionary
risky_asset_parms = {
# Risky return factor moments. Based on SP500 real returns from Shiller's
# "chapter 26" data, which can be found at http://www.econ.yale.edu/~shiller/data.htm
"RiskyAvg": 1.080370891,
"RiskyStd": 0.177196585,
# Number of integration nodes to use in approximation of risky returns
"RiskyCount": 5,
# Probability that the agent can adjust their portfolio each period
"AdjustPrb": 1.0,
}
# Make a dictionary to specify a risky asset consumer type
init_risky_asset = init_idiosyncratic_shocks.copy()
init_risky_asset.update(risky_asset_parms)