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).
Parameters
----------
init_file: yaml
Initialization file containing parameters for the estimation
process.
Returns
------
rslt: dict
Result dictionary containing
- quantiles
- mte
- mte_x
- mte_u
- mte_min
- mte_max
- X
- b1
- b0
"""
# Load the estimation 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:
# 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 simulate_test_data():
"""
Simulate test dict_ and data.
"""
fname = TEST_RESOURCES_DIR + "/tutorial.grmpy.yml"
data = simulate(fname)
dict_ = read(fname)
dict_, data = check_append_constant(
TEST_RESOURCES_DIR + "/tutorial.grmpy.yml", dict_, data, semipar=True
)
return dict_, data
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 plot_mte(
rslt,
init_file,
college_years=4,
font_size=22,
label_size=16,
color="blue",
semipar=False,
nboot=250,
save_plot=False,
):
"""This function calculates the marginal treatment effect for
different quantiles u_D of the unobservables.
Depending on the model specification, either the parametric or
semiparametric MTE is plotted along with the corresponding
90 percent confidence bands.
"""
# Read init dict and data
dict_ = read(init_file, semipar)
data = read_data(dict_["ESTIMATION"]["file"])
dict_, data = check_append_constant(init_file, dict_, data, semipar)
if semipar is True:
quantiles, mte, con_u, con_d = mte_and_cof_int_semipar(
rslt, init_file, college_years, nboot
)
else:
quantiles, mte, con_u, con_d = mte_and_cof_int_par(
rslt, dict_, data, college_years
)
# Add confidence intervals to rslt dictionary
rslt.update({"con_u": con_u, "con_d": con_d})
plot_curve(mte, quantiles, con_u, con_d, font_size, label_size, color, save_plot)
def plot_mte(
rslt,
init_file,
college_years=4,
font_size=22,
label_size=16,
color="blue",
semipar=False,
nboot=250,
save_plot=False,
):
"""
This function calculates the marginal treatment effect for
different quantiles u_D of the unobservables.
Depending on the model specification, either the parametric or
semiparametric MTE is plotted along with the corresponding
90 percent confidence bands.
Parameters
----------
rslt: dict
Result dictionary returned by grmpy.fit().
init_file: yaml
Initialization file containing parameters for the estimation
process.
college_years: int, default is 4
Average duration of college degree. The MTE plotted will thus
refer to the returns per one year of college education.
font_size: int, default is 22
Font size of the MTE graph.
label_size: int, default is 16
Label size of the MTE graph
color: str, default is "blue"
Color of the MTE curve.
semipar: bool, default is False
Option to indicate the semiparametric estimation.
If semipar is False, the parametric normal model is assumed and
confidence intervals are computed analytically.
Else (semipar is True), confidence bands are bootstrapped.
nboot: int, default is 250
Only relevant for semiparametric estimation (semipar=True).
Number of of bootstrap iterations used to compute
confidence intervals.
save_plot: bool or str or PathLike or file-like object, default is False
If False, the resulting plot is shown but not saved.
If True, the MTE plot is saved as 'MTE_plot.png'.
Else, if a str or Pathlike or file-like object is specified,
the plot is saved according to *save_plot*.
The output format is inferred from the extension ('png', 'pdf', 'svg'... etc.)
By default, '.png' is assumed.
"""
# Read init dict and data
dict_ = read(init_file, semipar)
data = read_data(dict_["ESTIMATION"]["file"])
dict_, data = check_append_constant(init_file, dict_, data, semipar)
if semipar is True:
quantiles, mte, con_u, con_d = mte_and_cof_int_semipar(
rslt, init_file, college_years, nboot)
else:
quantiles, mte, con_u, con_d = mte_and_cof_int_par(
rslt, dict_, data, college_years)
# Add confidence intervals to rslt dictionary
rslt.update({"con_u": con_u, "con_d": con_d})
plot_curve(mte, quantiles, con_u, con_d, font_size, label_size, color,
save_plot)