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()