def update(self):
'''
Update the income process, the assets grid, and the terminal solution.
Parameters
----------
none
Returns
-------
none
'''
orig_flow = self.time_flow
if self.cycles == 0: # hacky fix for labor supply l_bar
self.updateIncomeProcessAlt()
else:
self.updateIncomeProcess()
self.updateAssetsGrid()
self.updateSolutionTerminal()
self.timeFwd()
self.resetRNG()
if self.cycles > 0:
self.IncomeDstn = Model.applyFlatIncomeTax(self.IncomeDstn,
tax_rate=self.tax_rate,
T_retire=self.T_retire,
unemployed_indices=range(0,(self.TranShkCount+1)*
self.PermShkCount,self.TranShkCount+1))
self.makeIncShkHist()
if not orig_flow:
self.timeRev()
def update(self):
'''
Update the income process and the assets grid.
'''
orig_flow = self.time_flow
self.updateIncomeProcess()
self.updateAssetsGrid()
if self.cycles > 0:
self.income_distrib = Model.applyFlatIncomeTax(
self.income_distrib,
tax_rate=self.tax_rate,
T_retire=self.T_retire,
unemployed_indices=range(0, (self.xi_N + 1) * self.psi_N,
self.xi_N + 1))
scriptR_shocks, xi_shocks = Model.generateIncomeShockHistoryLognormalUnemployment(
self)
self.addIncomeShockPaths(scriptR_shocks, xi_shocks)
else:
self.timeRev()
scriptR_shocks, xi_shocks = Model.generateIncomeShockHistoryInfiniteSimple(
self)
self.addIncomeShockPaths(scriptR_shocks, xi_shocks)
if orig_flow:
self.timeFwd()
def simulateMPC(self):
original_time = self.time_flow
self.timeFwd()
t_first = 0
t_last = self.sim_periods
if self.cycles > 0:
cFuncs = self.cFunc[t_first:t_last]
else:
cFuncs = t_last * self.cFunc
simulated_kappa_matrix = Model.simulateMPChistory(
cFuncs, self.w0, self.perm_shocks[t_first:t_last],
self.temp_shocks[t_first:t_last])
if not original_time:
self.timeRev()
self.kappa_history = simulated_kappa_matrix
def update(self):
'''
Update the income process and the assets grid.
'''
orig_flow = self.time_flow
if self.cycles == 0: # hacky fix for labor supply l_bar
self.updateIncomeProcessAlt()
else:
self.updateIncomeProcess()
self.updateAssetsGrid()
self.updateSolutionTerminal()
self.timeFwd()
if self.cycles > 0:
self.income_distrib = Model.applyFlatIncomeTax(self.income_distrib,
tax_rate=self.tax_rate,
T_retire=self.T_retire,
unemployed_indices=range(0,(self.xi_N+1)*self.psi_N,self.xi_N+1))
scriptR_shocks, xi_shocks = Model.generateIncomeShockHistoryLognormalUnemployment(self)
self.addIncomeShockPaths(scriptR_shocks,xi_shocks)
else:
scriptR_shocks, xi_shocks = Model.generateIncomeShockHistoryInfiniteSimple(self)
self.addIncomeShockPaths(scriptR_shocks,xi_shocks)
if not orig_flow:
self.timeRev()
from HARKestimation import minimizeNelderMead, bootstrapSampleFromData
import numpy as np
import pylab
from time import time
# Set booleans to determine which tasks should be done
estimate_model = True
compute_standard_errors = False
make_contour_plot = True
#=====================================================
# Define objects and functions used for the estimation
#=====================================================
# Make a lifecycle consumer to be used for estimation, including simulated shocks (plus an initial distribution of wealth)
EstimationAgent = Model.ConsumerType(**Params.init_consumer_objects)
EstimationAgent(sim_periods=EstimationAgent.T_total + 1)
EstimationAgent.makeIncShkHist()
EstimationAgent.a_init = drawDiscrete(
P=Params.initial_wealth_income_ratio_probs,
X=Params.initial_wealth_income_ratio_vals,
N=Params.num_agents,
seed=Params.seed)
# Define the objective function for the estimation
def smmObjectiveFxn(DiscFacAdj,
CRRA,
agent=EstimationAgent,
DiscFacAdj_bound=Params.DiscFacAdj_bound,
CRRA_bound=Params.CRRA_bound,
import sys
from HARKutilities import plotFunc, plotFuncDer, plotFuncs # Basic plotting tools
from time import clock # Timing utility
from copy import deepcopy # "Deep" copying for complex objects
from HARKparallel import multiThreadCommandsFake, multiThreadCommands # Parallel processing
mystr = lambda number: "{:.4f}".format(number) # Format numbers as strings
import numpy as np # Numeric Python
if __name__ == '__main__': # Parallel calls *must* be inside a call to __main__
type_count = 32 # Number of values of CRRA to solve
# Make the basic type that we'll use as a template.
# The basic type has an artificially dense assets grid, as the problem to be
# solved must be sufficiently large for multithreading to be faster than
# single-threading (looping), due to overhead.
BasicType = Model.ConsumerType(**Params.init_consumer_objects)
BasicType.aXtraMax = 100
BasicType.aXtraCount = 64
BasicType.updateAssetsGrid()
BasicType.timeFwd()
BasicType.assignParameters(
LivPrb=[0.98], DiscFac=[0.96], PermGroFac=[1.01],
cycles=0) # This is what makes the type infinite horizon
BasicType(vFuncBool=False, cubicBool=True)
BasicType.IncomeDstn = [
BasicType.IncomeDstn[0]
] # A "one period" infinite horizon model, so only need one period of income distributions
# Solve the basic type and plot the results, to make sure things are working
start_time = clock()
BasicType.solve()
from HARKestimation import minimizeNelderMead, bootstrapSampleFromData # Estimation methods
import numpy as np # Numeric Python
import pylab # Python reproductions of some Matlab functions
from time import time # Timing utility
# Set booleans to determine which tasks should be done
estimate_model = True # Whether to estimate the model
compute_standard_errors = False # Whether to get standard errors via bootstrap
make_contour_plot = False # Whether to make a contour map of the objective function
#=====================================================
# Define objects and functions used for the estimation
#=====================================================
# Make a lifecycle consumer to be used for estimation, including simulated shocks (plus an initial distribution of wealth)
EstimationAgent = Model.IndShockConsumerType(**Params.init_consumer_objects) # Make a ConsumerType for estimation
<<<<<<< HEAD
EstimationAgent(sim_periods = EstimationAgent.T_total+1) # Set the number of periods to simulate
EstimationAgent.makeIncShkHist() # Make a simulated history of income shocks for many consumers
EstimationAgent.a_init = drawDiscrete(P=Params.initial_wealth_income_ratio_probs,
X=Params.initial_wealth_income_ratio_vals,
N=Params.num_agents,
=======
EstimationAgent.time_inv.remove('DiscFac') # This estimation uses age-varying discount factors as
EstimationAgent.time_vary.append('DiscFac') # estimated by Cagetti (2003), so switch from time_inv to time_vary
EstimationAgent(sim_periods = EstimationAgent.T_total+1) # Set the number of periods to simulate
EstimationAgent.makeIncShkHist() # Make a simulated history of income shocks for many consumers
EstimationAgent.a_init = drawDiscrete(N=Params.num_agents,
P=Params.initial_wealth_income_ratio_probs,
X=Params.initial_wealth_income_ratio_vals,
>>>>>>> eeb37f24755d0c683c9d9efbe5e7447425c98b86
from time import time
# Set booleans to determine which tasks should be done
estimate_model = True
compute_standard_errors = False
make_contour_plot = False
#=====================================================
# Define objects and functions used for the estimation
#=====================================================
# Make a lifecycle consumer to be used for estimation, including simulated shocks
EstimationAgent = Model.ConsumerType(**Params.init_consumer_objects)
# Make histories of permanent and transitory shocks, plus an initial distribution of wealth
scriptR_shocks, xi_shocks = Model.generateIncomeShockHistoryLognormalUnemployment(EstimationAgent)
w0_vector = generateDiscreteDraws(P=Params.initial_wealth_income_ratio_probs,
X=Params.initial_wealth_income_ratio_vals,
N=Params.num_agents,
seed=Params.seed)
EstimationAgent.addIncomeShockPaths(scriptR_shocks,xi_shocks)
# Define the objective function for the estimation
def smmObjectiveFxn(beth, rho,
agent = EstimationAgent,
beth_bound = Params.beth_bound,
rho_bound = Params.rho_bound,
empirical_data = Data.w_to_y_data,
empirical_weights = Data.empirical_weights,
empirical_groups = Data.empirical_groups,
initial_wealth = w0_vector,
