do_optimizing = False # Set to True to reestimate the distribution of time preference rates
if do_optimizing: # If you want to rerun the cstwMPC estimation, change do_optimizing to True
# Finite value requires discount factor from combined pure and mortality-induced
# discounting to be less than one, so maximum DiscFac is 1/LivPrb
DiscFacMax = 1 / Params.init_cjSOE['LivPrb'][0] #
param_range = [0.995, -0.0001 + DiscFacMax]
spread_range = [0.00195, 0.0205] #
if Params.do_param_dist: # If configured to estimate the distribution
LorenzBool = True
# Run the param-dist estimation
paramDistObjective = lambda spread: cstwMPC.findLorenzDistanceAtTargetKY(
Economy=EstimationEconomy,
param_name=Params.param_name,
param_count=Params.pref_type_count,
center_range=param_range,
spread=spread,
dist_type=Params.dist_type) # Distribution of DiscFac
t_start = clock()
spread_estimate = golden(paramDistObjective,
brack=spread_range,
tol=1e-4)
center_estimate = EstimationEconomy.center_save
t_end = clock()
else: # Run the param-point estimation only
paramPointObjective = lambda center: cstwMPC.getKYratioDifference(
Economy=EstimationEconomy,
param_name=Params.param_name,
param_count=Params.pref_type_count,
if param_name == 'CRRA':
param_range = [0.2, 70.0]
spread_range = [0.00001, 1.0]
elif param_name == 'DiscFac':
param_range = [0.95, 0.995]
spread_range = [0.006, 0.008]
else:
print('Parameter range for ' + param_name +
' has not been defined!')
if do_param_dist:
# Run the param-dist estimation
paramDistObjective = lambda spread: findLorenzDistanceAtTargetKY(
Economy=EstimationEconomy,
param_name=param_name,
param_count=pref_type_count,
center_range=param_range,
spread=spread,
dist_type=dist_type)
t_start = clock()
spread_estimate = (minimize_scalar(paramDistObjective,
bracket=spread_range,
tol=1e-4,
method='brent')).x
center_estimate = EstimationEconomy.center_save
t_end = clock()
else:
# Run the param-point estimation only
paramPointObjective = lambda center: getKYratioDifference(
Economy=EstimationEconomy,
param_name=param_name,