def read(file_):
"""The function reads the initialization file and returns a dictionary with parameters for the
simulation.
"""
check_presence_init(file_)
dict_ = {'varnames': []}
for line in open(file_).readlines():
list_ = shlex.split(line)
is_empty = (list_ == [])
if not is_empty:
is_keyword = list_[0].isupper()
else:
continue
if is_keyword:
keyword = list_[0]
dict_[keyword] = {}
continue
process(list_, dict_, keyword)
dict_ = auxiliary(dict_)
return dict_
def read(file, semipar=False, include_constant=False):
"""This function processes the initialization file
for the estimation process.
"""
# Check if there is an init file with the specified filename
check_presence_init(file)
# Load the initialization file
with open(file) as y:
init_dict = yaml.load(y, Loader=yaml.FullLoader)
# If missing, add generic covariance matrix of the unobservables
if semipar is False:
try:
init_dict["DIST"]
except KeyError:
init_dict["DIST"] = {
"params": np.array([0.1, 0.0, 0.0, 0.1, 0.0, 1.0])
}
else:
pass
else:
pass
# Process the initialization file
attr_dict = create_attr_dict_est(init_dict, semipar, include_constant)
return attr_dict
def par_fit(init_file):
"""The function estimates the coefficients of the simulated data set."""
check_presence_init(init_file)
dict_ = read(init_file)
np.random.seed(dict_["SIMULATION"]["seed"])
# We perform some basic consistency checks regarding the user's request.
check_presence_estimation_dataset(dict_)
#check_initialization_dict2(dict_)
#check_init_file(dict_)
# Distribute initialization information.
data = read_data(dict_["ESTIMATION"]["file"])
num_treated = dict_["AUX"]["num_covars_treated"]
num_untreated = num_treated + dict_["AUX"]["num_covars_untreated"]
_, X1, X0, Z1, Z0, Y1, Y0 = process_data(data, dict_)
if dict_["ESTIMATION"]["maxiter"] == 0:
option = "init"
else:
option = dict_["ESTIMATION"]["start"]
# Read data frame
# define starting values
x0 = start_values(dict_, data, option)
opts, method = optimizer_options(dict_)
dict_["AUX"]["criteria"] = calculate_criteria(dict_, X1, X0, Z1, Z0, Y1,
Y0, x0)
dict_["AUX"]["starting_values"] = backward_transformation(x0)
rslt_dict = bfgs_dict()
if opts["maxiter"] == 0:
rslt = adjust_output(None, dict_, x0, X1, X0, Z1, Z0, Y1, Y0,
rslt_dict)
else:
opt_rslt = minimize(
minimizing_interface,
x0,
args=(dict_, X1, X0, Z1, Z0, Y1, Y0, num_treated, num_untreated,
rslt_dict),
method=method,
options=opts,
)
rslt = adjust_output(opt_rslt, dict_, opt_rslt["x"], X1, X0, Z1, Z0,
Y1, Y0, rslt_dict)
# Print Output files
print_logfile(dict_, rslt)
if "comparison" in dict_["ESTIMATION"].keys():
if dict_["ESTIMATION"]["comparison"] == 0:
pass
else:
write_comparison(data, rslt)
else:
write_comparison(data, rslt)
return rslt
def read_simulation(file):
"""Process initialization file for the simulation."""
# Check if there is an init file with the specified filename
check_presence_init(file)
# Load the initialization file
with open(file) as y:
init_dict = yaml.load(y, Loader=yaml.FullLoader)
# Process the initialization file
attr_dict = create_attr_dict_sim(init_dict)
return attr_dict
def estimate(init_file):
"""The function estimates the coefficients of the simulated data set."""
check_presence_init(init_file)
dict_ = read(init_file)
np.random.seed(dict_['SIMULATION']['seed'])
# We perform some basic consistency checks regarding the user's request.
check_presence_estimation_dataset(dict_)
check_initialization_dict(dict_)
check_init_file(dict_)
# Distribute initialization information.
data_file = dict_['ESTIMATION']['file']
if dict_['ESTIMATION']['maxiter'] == 0:
option = 'init'
else:
option = dict_['ESTIMATION']['start']
# Read data frame
data = read_data(data_file)
# define starting values
x0 = start_values(dict_, data, option)
opts, method = optimizer_options(dict_)
dict_['AUX']['criteria'] = calculate_criteria(dict_, data, x0)
dict_['AUX']['starting_values'] = backward_transformation(x0)
rslt_dict = bfgs_dict()
if opts['maxiter'] == 0:
rslt = adjust_output(None, dict_, x0, data, rslt_dict)
else:
opt_rslt = minimize(minimizing_interface,
x0,
args=(dict_, data, rslt_dict),
method=method,
options=opts)
rslt = adjust_output(opt_rslt, dict_, opt_rslt['x'], data, rslt_dict)
# Print Output files
print_logfile(dict_, rslt)
if 'comparison' in dict_['ESTIMATION'].keys():
if dict_['ESTIMATION']['comparison'] == 0:
pass
else:
write_comparison(dict_, data, rslt)
else:
write_comparison(dict_, data, rslt)
return rslt
def read(file, semipar=False, include_constant=False):
"""This function processes the initialization file
for the estimation process.
"""
# Check if there is a init file with the specified filename
check_presence_init(file)
# Load the initialization file
with open(file) as y:
init_dict = yaml.load(y, Loader=yaml.FullLoader)
# Process the initialization file
attr_dict = create_attr_dict_est(init_dict, semipar, include_constant)
return attr_dict
def fit(init_file, semipar=False):
""" """
check_presence_init(init_file)
dict_ = read(init_file)
# Perform some consistency checks given the user's request
check_presence_estimation_dataset(dict_)
check_initialization_dict(dict_)
# Semiparametric Model
if semipar is True:
quantiles, mte_u, X, b1_b0 = semipar_fit(init_file) # change to dict_
# Construct MTE
# Calculate the MTE component that depends on X
mte_x = np.dot(X, b1_b0)
# Put the MTE together
mte = mte_x.mean(axis=0) + mte_u
# Accounting for variation in X
mte_min = np.min(mte_x) + mte_u
mte_max = np.max(mte_x) + mte_u
rslt = {
"quantiles": quantiles,
"mte": mte,
"mte_x": mte_x,
"mte_u": mte_u,
"mte_min": mte_min,
"mte_max": mte_max,
"X": X,
"b1-b0": b1_b0,
}
# Parametric Normal Model
else:
check_par(dict_)
rslt = par_fit(dict_)
return rslt
def fit(init_file, semipar=False):
"""This function estimates the MTE based on a parametric normal model
or, alternatively, via the semiparametric method of
local instrumental variables (LIV).
"""
# Load the estimation file
check_presence_init(init_file)
dict_ = read(init_file, semipar)
# Perform some consistency checks given the user's request
check_presence_estimation_dataset(dict_)
check_est_init_dict(dict_)
# Semiparametric LIV Model
if semipar is True:
# Distribute initialization information.
data = read_data(dict_["ESTIMATION"]["file"])
dict_, data = check_append_constant(init_file,
dict_,
data,
semipar=True)
rslt = semipar_fit(dict_, data)
# Parametric Normal Model
else:
# Perform some extra checks
check_par_init_file(dict_)
# Distribute initialization information.
data = read_data(dict_["ESTIMATION"]["file"])
dict_, data = check_append_constant(init_file,
dict_,
data,
semipar=False)
rslt = par_fit(dict_, data)
return rslt
def bootstrap(init_file, nboot):
"""
This function generates bootsrapped standard errors
given an init_file and the number of bootstraps to be drawn.
Parameters
----------
init_file: yaml
Initialization file containing parameters for the estimation
process.
nboot: int
Number of bootstrap iterations, i.e. number of times
the MTE is computed via bootstrap.
Returns
-------
mte_boot: np.ndarray
Array containing *nbootstrap* estimates of the MTE.
"""
check_presence_init(init_file)
dict_ = read(init_file, semipar=True)
# Process the information specified in the initialization file
bins, logit, bandwidth, gridsize, startgrid, endgrid = process_primary_inputs(
dict_)
trim, rbandwidth, reestimate_p, show_output = process_secondary_inputs(
dict_)
# Suppress output
show_output = False
# Prepare empty array to store output values
mte_boot = np.zeros([gridsize, nboot])
# Load the baseline data
data = read_data(dict_["ESTIMATION"]["file"])
counter = 0
while counter < nboot:
boot_data = resample(data,
replace=True,
n_samples=len(data),
random_state=None)
# Estimate propensity score P(z)
boot_data = estimate_treatment_propensity(dict_, boot_data, logit,
show_output)
prop_score = boot_data["prop_score"]
if isinstance(prop_score, pd.Series):
# Define common support and trim the data (if trim=True)
X, Y, prop_score = trim_support(dict_,
boot_data,
logit,
bins,
trim,
reestimate_p,
show_output=False)
b0, b1_b0 = double_residual_reg(X, Y, prop_score)
# # Construct the MTE
mte_x = mte_observed(X, b1_b0)
mte_u = mte_unobserved_semipar(X, Y, b0, b1_b0, prop_score,
bandwidth, gridsize, startgrid,
endgrid)
# Put the MTE together
mte = mte_x.mean(axis=0) + mte_u
mte_boot[:, counter] = mte
counter += 1
else:
continue
return mte_boot
def semipar_fit(init_file):
"""This functions estimates the MTE via Local Instrumental Variables"""
check_presence_init(init_file)
dict_ = read(init_file)
# np.random.seed(dict_["SIMULATION"]["seed"]) # needed?
check_presence_estimation_dataset(dict_)
check_initialization_dict(dict_)
# Distribute initialization information.
data = read_data(dict_["ESTIMATION"]["file"])
# Process data for the semiparametric estimation.
indicator = dict_["ESTIMATION"]["indicator"]
D = data[indicator].values
Z = data[dict_["CHOICE"]["order"]]
nbins = dict_["ESTIMATION"]["nbins"]
trim = dict_["ESTIMATION"]["trim_support"]
reestimate = dict_["ESTIMATION"]["reestimate_p"]
rbandwidth = dict_["ESTIMATION"]["rbandwidth"]
bandwidth = dict_["ESTIMATION"]["bandwidth"]
gridsize = dict_["ESTIMATION"]["gridsize"]
a = dict_["ESTIMATION"]["ps_range"][0]
b = dict_["ESTIMATION"]["ps_range"][1]
logit = dict_["ESTIMATION"]["logit"]
show_output = dict_["ESTIMATION"]["show_output"]
# The Local Instrumental Variables (LIV) approach
# 1. Estimate propensity score P(z)
ps = estimate_treatment_propensity(D, Z, logit, show_output)
# 2a. Find common support
treated, untreated, common_support = define_common_support(
ps, indicator, data, nbins, show_output
)
# 2b. Trim the data
if trim is True:
data, ps = trim_data(ps, common_support, data)
# 2c. Re-estimate baseline propensity score on the trimmed sample
if reestimate is True:
D = data[indicator].values
Z = data[dict_["CHOICE"]["order"]]
# Re-estimate propensity score P(z)
ps = estimate_treatment_propensity(D, Z, logit, show_output)
# 3. Double Residual Regression
# Sort data by ps
data = data.sort_values(by="ps", ascending=True)
ps = np.sort(ps)
X = data[dict_["TREATED"]["order"]]
Xp = construct_Xp(X, ps)
Y = data[[dict_["ESTIMATION"]["dependent"]]]
b0, b1_b0 = double_residual_reg(ps, X, Xp, Y, rbandwidth, show_output)
# Turn the X, Xp, and Y DataFrames into np.ndarrays
X_arr = np.array(X)
Xp_arr = np.array(Xp)
Y_arr = np.array(Y).ravel()
# 4. Compute the unobserved part of Y
Y_tilde = Y_arr - np.dot(X_arr, b0) - np.dot(Xp_arr, b1_b0)
# 5. Estimate mte_u, the unobserved component of the MTE,
# through a locally quadratic regression
quantiles, mte_u = locpoly(ps, Y_tilde, 1, 2, bandwidth, gridsize, a, b)
# 6. construct MTE
# Calculate the MTE component that depends on X
# mte_x = np.dot(X, b1_b0).mean(axis=0)
# Put the MTE together
# mte = mte_x + mte_u
return quantiles, mte_u, X, b1_b0
def bootstrap(init_file, nbootstraps):
"""
This function generates bootsrapped standard errors
given an init_file and the number of bootsraps to be drawn.
"""
check_presence_init(init_file)
dict_ = read(init_file, semipar=True)
# Process the information specified in the initialization file
nbins, logit, bandwidth, gridsize, a, b = process_user_input(dict_)
trim, rbandwidth, reestimate_p = process_default_input(dict_)
# Suppress output
show_output = False
# Prepare empty array to store output values
mte_boot = np.zeros([gridsize, nbootstraps])
# Load the baseline data
data = read_data(dict_["ESTIMATION"]["file"])
counter = 0
while counter < nbootstraps:
boot_data = resample(data,
replace=True,
n_samples=len(data),
random_state=None)
# Process the inputs for the decision equation
indicator, D, Z = process_choice_data(dict_, boot_data)
# Estimate propensity score P(z)
ps = estimate_treatment_propensity(D, Z, logit, show_output)
if isinstance(ps, np.ndarray):
# Define common support and trim the data, if trim=True
boot_data, ps = trim_support(
dict_,
boot_data,
logit,
ps,
indicator,
nbins,
trim,
reestimate_p,
show_output,
)
# Estimate the observed and unobserved component of the MTE
X, b1_b0, b0, mte_u = mte_components(dict_, boot_data, ps,
rbandwidth, bandwidth,
gridsize, a, b, show_output)
# Calculate the MTE component that depends on X
mte_x = np.dot(X, b1_b0).mean(axis=0)
# Put the MTE together
mte = mte_x + mte_u
mte_boot[:, counter] = mte
counter += 1
else:
continue
return mte_boot
def bootstrap(init_file, nbootstraps, show_output=False):
"""
This function generates bootsrapped standard errors
given an init_file and the number of bootsraps to be drawn.
"""
check_presence_init(init_file)
dict_ = read(init_file)
nbins = dict_["ESTIMATION"]["nbins"]
trim = dict_["ESTIMATION"]["trim_support"]
rbandwidth = dict_["ESTIMATION"]["rbandwidth"]
bandwidth = dict_["ESTIMATION"]["bandwidth"]
gridsize = dict_["ESTIMATION"]["gridsize"]
a = dict_["ESTIMATION"]["ps_range"][0]
b = dict_["ESTIMATION"]["ps_range"][1]
logit = dict_["ESTIMATION"]["logit"]
# Distribute initialization information.
data = read_data(dict_["ESTIMATION"]["file"])
# Prepare empty arrays to store output values
mte_boot = np.zeros([gridsize, nbootstraps])
counter = 0
while counter < nbootstraps:
boot = resample(data, replace=True, n_samples=len(data), random_state=None)
# Process data for the semiparametric estimation.
indicator = dict_["ESTIMATION"]["indicator"]
D = boot[indicator].values
Z = boot[dict_["CHOICE"]["order"]]
# The Local Instrumental Variables (LIV) approach
# 1. Estimate propensity score P(z)
ps = estimate_treatment_propensity(D, Z, logit, show_output)
if isinstance(ps, np.ndarray): # & (np.min(ps) <= 0.3) & (np.max(ps) >= 0.7):
# 2a. Find common support
treated, untreated, common_support = define_common_support(
ps, indicator, boot, nbins, show_output
)
# 2b. Trim the data
if trim is True:
boot, ps = trim_data(ps, common_support, boot)
# 3. Double Residual Regression
# Sort data by ps
boot = boot.sort_values(by="ps", ascending=True)
ps = np.sort(ps)
X = boot[dict_["TREATED"]["order"]]
Xp = construct_Xp(X, ps)
Y = boot[[dict_["ESTIMATION"]["dependent"]]]
b0, b1_b0 = double_residual_reg(ps, X, Xp, Y, rbandwidth, show_output)
# Turn the X, Xp, and Y DataFrames into np.ndarrays
X_arr = np.array(X)
Xp_arr = np.array(Xp)
Y_arr = np.array(Y).ravel()
# 4. Compute the unobserved part of Y
Y_tilde = Y_arr - np.dot(X_arr, b0) - np.dot(Xp_arr, b1_b0)
# 5. Estimate mte_u, the unobserved component of the MTE,
# through a locally quadratic regression
quantiles, mte_u = locpoly(ps, Y_tilde, 1, 2, bandwidth, gridsize, a, b)
# 6. construct MTE
# Calculate the MTE component that depends on X
mte_x = np.dot(X, b1_b0).mean(axis=0)
# Put the MTE together
mte = mte_x + mte_u
mte_boot[:, counter] = mte
counter += 1
else:
continue
return mte_boot
