def calculateStandardErrorsByBootstrap(initial_estimate,
N,
seed=0,
verbose=False):
'''
Estimates the SolvingMicroDSOPs model N times, then calculates standard errors.
'''
t_0 = time()
# Generate a list of seeds for generating bootstrap samples
RNG = np.random.RandomState(seed)
seed_list = RNG.randint(2**31 - 1, size=N)
# Estimate the model N times, recording each set of estimated parameters
estimate_list = []
for n in range(N):
t_start = time()
# Bootstrap a new dataset by sampling
bootstrap_data = (bootstrapSampleFromData(Data.scf_data_array,
seed=seed_list[n])).T
w_to_y_data_bootstrap = bootstrap_data[0, ]
empirical_groups_bootstrap = bootstrap_data[1, ]
empirical_weights_bootstrap = bootstrap_data[2, ]
# Make a temporary function for use in this estimation run
smmObjectiveFxnBootstrap = lambda parameters_to_estimate: smmObjectiveFxn(
DiscFacAdj=parameters_to_estimate[0],
CRRA=parameters_to_estimate[1],
empirical_data=w_to_y_data_bootstrap,
empirical_weights=empirical_weights_bootstrap,
empirical_groups=empirical_groups_bootstrap)
# Estimate the model with the bootstrap data and add to list of estimates
this_estimate = minimizeNelderMead(smmObjectiveFxnBootstrap,
initial_estimate)
estimate_list.append(this_estimate)
t_now = time()
# Report progress of the bootstrap
if verbose:
print('Finished bootstrap estimation #' + str(n + 1) + ' of ' +
str(N) + ' in ' + str(t_now - t_start) + ' seconds (' +
str(t_now - t_0) + ' cumulative)')
# Calculate the standard errors for each parameter
estimate_array = (np.array(estimate_list)).T
DiscFacAdj_std_error = np.std(estimate_array[0])
CRRA_std_error = np.std(estimate_array[1])
return [DiscFacAdj_std_error, CRRA_std_error]
def calculateStandardErrorsByBootstrap(initial_estimate,N,seed=0,verbose=False):
'''
Estimates the SolvingMicroDSOPs model N times, then calculates standard errors.
'''
t_0 = time()
# Generate a list of seeds for generating bootstrap samples
RNG = np.random.RandomState(seed)
seed_list = RNG.randint(2**31-1,size=N)
# Estimate the model N times, recording each set of estimated parameters
estimate_list = []
for n in range(N):
t_start = time()
# Bootstrap a new dataset by sampling
bootstrap_data = (bootstrapSampleFromData(Data.scf_data_array,seed=seed_list[n])).T
w_to_y_data_bootstrap = bootstrap_data[0,]
empirical_groups_bootstrap = bootstrap_data[1,]
empirical_weights_bootstrap = bootstrap_data[2,]
# Make a temporary function for use in this estimation run
smmObjectiveFxnBootstrap = lambda parameters_to_estimate : smmObjectiveFxn(DiscFacAdj=parameters_to_estimate[0],
CRRA=parameters_to_estimate[1],
empirical_data = w_to_y_data_bootstrap,
empirical_weights = empirical_weights_bootstrap,
empirical_groups = empirical_groups_bootstrap)
# Estimate the model with the bootstrap data and add to list of estimates
this_estimate = minimizeNelderMead(smmObjectiveFxnBootstrap,initial_estimate)
estimate_list.append(this_estimate)
t_now = time()
# Report progress of the bootstrap
if verbose:
print('Finished bootstrap estimation #' + str(n+1) + ' of ' + str(N) + ' in ' + str(t_now-t_start) + ' seconds (' + str(t_now-t_0) + ' cumulative)')
# Calculate the standard errors for each parameter
estimate_array = (np.array(estimate_list)).T
DiscFacAdj_std_error = np.std(estimate_array[0])
CRRA_std_error = np.std(estimate_array[1])
return [DiscFacAdj_std_error, CRRA_std_error]
standard_errors : [float,float]
Standard errors calculated by bootstrap: [DiscFacAdj_std_error, CRRA_std_error].
'''
t_0 = time()
# Generate a list of seeds for generating bootstrap samples
RNG = np.random.RandomState(seed)
seed_list = RNG.randint(2**31-1,size=N)
# Estimate the model N times, recording each set of estimated parameters
estimate_list = []
for n in range(N):
t_start = time()
# Bootstrap a new dataset by resampling from the original data
bootstrap_data = (bootstrapSampleFromData(Data.scf_data_array,seed=seed_list[n])).T
w_to_y_data_bootstrap = bootstrap_data[0,]
empirical_groups_bootstrap = bootstrap_data[1,]
empirical_weights_bootstrap = bootstrap_data[2,]
# Make a temporary function for use in this estimation run
smmObjectiveFxnBootstrap = lambda parameters_to_estimate : smmObjectiveFxn(DiscFacAdj=parameters_to_estimate[0],
CRRA=parameters_to_estimate[1],
empirical_data = w_to_y_data_bootstrap,
empirical_weights = empirical_weights_bootstrap,
empirical_groups = empirical_groups_bootstrap)
# Estimate the model with the bootstrap data and add to list of estimates
this_estimate = minimizeNelderMead(smmObjectiveFxnBootstrap,initial_estimate)
estimate_list.append(this_estimate)
t_now = time()
def calculateStandardErrorsByBootstrap(initial_estimate,N,seed=0,verbose=False):
'''
Calculates standard errors by repeatedly re-estimating the model with datasets
resampled from the actual data.
Parameters
----------
initial_estimate : [float,float]
The estimated [DiscFacAdj,CRRA], for use as an initial guess for each
re-estimation in the bootstrap procedure.
N : int
Number of times to resample data and re-estimate the model.
seed : int
Seed for the random number generator.
verbose : boolean
Indicator for whether extra output should be printed for the user.
Returns
-------
standard_errors : [float,float]
Standard errors calculated by bootstrap: [DiscFacAdj_std_error, CRRA_std_error].
'''
t_0 = time()
# Generate a list of seeds for generating bootstrap samples
RNG = np.random.RandomState(seed)
seed_list = RNG.randint(2**31-1,size=N)
# Estimate the model N times, recording each set of estimated parameters
estimate_list = []
for n in range(N):
t_start = time()
# Bootstrap a new dataset by resampling from the original data
bootstrap_data = (bootstrapSampleFromData(Data.scf_data_array,seed=seed_list[n])).T
w_to_y_data_bootstrap = bootstrap_data[0,]
empirical_groups_bootstrap = bootstrap_data[1,]
empirical_weights_bootstrap = bootstrap_data[2,]
# Make a temporary function for use in this estimation run
smmObjectiveFxnBootstrap = lambda parameters_to_estimate : smmObjectiveFxn(DiscFacAdj=parameters_to_estimate[0],
CRRA=parameters_to_estimate[1],
empirical_data = w_to_y_data_bootstrap,
empirical_weights = empirical_weights_bootstrap,
empirical_groups = empirical_groups_bootstrap)
# Estimate the model with the bootstrap data and add to list of estimates
this_estimate = minimizeNelderMead(smmObjectiveFxnBootstrap,initial_estimate)
estimate_list.append(this_estimate)
t_now = time()
# Report progress of the bootstrap
if verbose:
print('Finished bootstrap estimation #' + str(n+1) + ' of ' + str(N) + ' in ' + str(t_now-t_start) + ' seconds (' + str(t_now-t_0) + ' cumulative)')
# Calculate the standard errors for each parameter
estimate_array = (np.array(estimate_list)).T
DiscFacAdj_std_error = np.std(estimate_array[0])
CRRA_std_error = np.std(estimate_array[1])
return [DiscFacAdj_std_error, CRRA_std_error]
def calculateStandardErrorsByBootstrap(initial_estimate,
N,
seed=0,
verbose=False):
'''
Calculates standard errors by repeatedly re-estimating the model with datasets
resampled from the actual data.
Parameters
----------
initial_estimate : [float,float]
The estimated [DiscFacAdj,CRRA], for use as an initial guess for each
re-estimation in the bootstrap procedure.
N : int
Number of times to resample data and re-estimate the model.
seed : int
Seed for the random number generator.
verbose : boolean
Indicator for whether extra output should be printed for the user.
Returns
-------
standard_errors : [float,float]
Standard errors calculated by bootstrap: [DiscFacAdj_std_error, CRRA_std_error].
'''
t_0 = time()
# Generate a list of seeds for generating bootstrap samples
RNG = np.random.RandomState(seed)
seed_list = RNG.randint(2**31 - 1, size=N)
# Estimate the model N times, recording each set of estimated parameters
estimate_list = []
for n in range(N):
t_start = time()
# Bootstrap a new dataset by resampling from the original data
bootstrap_data = (bootstrapSampleFromData(Data.scf_data_array,
seed=seed_list[n])).T
w_to_y_data_bootstrap = bootstrap_data[0, ]
empirical_groups_bootstrap = bootstrap_data[1, ]
empirical_weights_bootstrap = bootstrap_data[2, ]
# Make a temporary function for use in this estimation run
smmObjectiveFxnBootstrap = lambda parameters_to_estimate: smmObjectiveFxn(
DiscFacAdj=parameters_to_estimate[0],
CRRA=parameters_to_estimate[1],
empirical_data=w_to_y_data_bootstrap,
empirical_weights=empirical_weights_bootstrap,
empirical_groups=empirical_groups_bootstrap)
# Estimate the model with the bootstrap data and add to list of estimates
this_estimate = minimizeNelderMead(smmObjectiveFxnBootstrap,
initial_estimate)
estimate_list.append(this_estimate)
t_now = time()
# Report progress of the bootstrap
if verbose:
print('Finished bootstrap estimation #' + str(n + 1) + ' of ' +
str(N) + ' in ' + str(t_now - t_start) + ' seconds (' +
str(t_now - t_0) + ' cumulative)')
# Calculate the standard errors for each parameter
estimate_array = (np.array(estimate_list)).T
DiscFacAdj_std_error = np.std(estimate_array[0])
CRRA_std_error = np.std(estimate_array[1])
return [DiscFacAdj_std_error, CRRA_std_error]