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, 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()
import ConsIndShockModel as Model # Consumption-saving model with idiosyncratic shocks
from HARKutilities import plotFuncs, plotFuncsDer # 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.IndShockConsumerType(**Params.init_idiosyncratic_shocks)
BasicType.cycles = 0
BasicType(aXtraMax = 100, aXtraCount = 64)
BasicType(vFuncBool = False, CubicBool = True)
BasicType.updateAssetsGrid()
BasicType.timeFwd()
# Solve the basic type and plot the results, to make sure things are working
start_time = clock()
BasicType.solve()
end_time = clock()
print('Solving the basic consumer took ' + mystr(end_time-start_time) + ' seconds.')
BasicType.unpackcFunc()
print('Consumption function:')
plotFuncs(BasicType.cFunc[0],0,5) # plot consumption
print('Marginal consumption function:')
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
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,
seed=Params.seed) # Draw initial assets for each consumer