def mte_and_cof_int_par(rslt, init_dict, data, college_years):
"""
This function returns the parametric MTE divided by the number
of college years, which represents the returns per YEAR of
post-secondary schooling.
90 percent confidence intervals are computed analytically.
Parameters
----------
rslt: dict
Result dictionary containing parameters for the estimation
process.
init_dict: dict
Initialization dictionary containing parameters for the
estimation process.
data: pandas.DataFrame
Data set containing the observables (explanatory and outcome variables)
analyzed in the generalized Roy framework.
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.
"""
# Define quantiles of u_D (unobserved resistance to treatment)
quantiles = [0.0001] + np.arange(0.01, 1.0, 0.01).tolist() + [0.9999]
# Calculate the MTE and confidence intervals
mte = calculate_mte(rslt, data, quantiles)
mte = [i / college_years for i in mte]
con_u, con_d = calculate_cof_int(rslt, init_dict, data, mte, quantiles)
return quantiles, mte, con_u, con_d
def plot_est_mte(rslt, file):
"""This function calculates the marginal treatment effect for different quartiles of the
unobservable V. ased on the calculation results."""
init_dict = read(file)
data_frame = pd.read_pickle(init_dict['ESTIMATION']['file'])
# Define the Quantiles and read in the original results
quantiles = [0.0001] + np.arange(0.01, 1., 0.01).tolist() + [0.9999]
mte_ = json.load(open('data/mte_original.json', 'r'))
mte_original = mte_[1]
mte_original_d = mte_[0]
mte_original_u = mte_[2]
# Calculate the MTE and confidence intervals
mte = calculate_mte(rslt, init_dict, data_frame, quantiles)
mte = [i / 4 for i in mte]
mte_up, mte_d = calculate_cof_int(rslt, init_dict, data_frame, mte,
quantiles)
# Plot both curves
ax = plt.figure(figsize=(17.5, 10)).add_subplot(111)
ax.set_ylabel(r"$B^{MTE}$", fontsize=24)
ax.set_xlabel("$u_D$", fontsize=24)
ax.tick_params(axis='both', which='major', labelsize=18)
ax.plot(quantiles, mte, label='grmpy $B^{MTE}$', color='blue', linewidth=4)
ax.plot(quantiles, mte_up, color='blue', linestyle=':', linewidth=3)
ax.plot(quantiles, mte_d, color='blue', linestyle=':', linewidth=3)
ax.plot(quantiles,
mte_original,
label='original$B^{MTE}$',
color='orange',
linewidth=4)
ax.plot(quantiles,
mte_original_d,
color='orange',
linestyle=':',
linewidth=3)
ax.plot(quantiles,
mte_original_u,
color='orange',
linestyle=':',
linewidth=3)
ax.set_ylim([-0.41, 0.51])
ax.set_xlim([-0.005, 1.005])
blue_patch = mpatches.Patch(color='blue', label='original $B^{MTE}$')
orange_patch = mpatches.Patch(color='orange', label='grmpy $B^{MTE}$')
plt.legend(handles=[blue_patch, orange_patch], prop={'size': 16})
plt.show()
return mte
def parametric_mte(rslt, file):
"""This function calculates the marginal treatment effect for different quartiles
of the unobservable V based on the calculation results."""
init_dict = read(file)
data_frame = pd.read_pickle(init_dict["ESTIMATION"]["file"])
# Define quantiles and read in the original results
quantiles = [0.0001] + np.arange(0.01, 1.0, 0.01).tolist() + [0.9999]
# Calculate the MTE and confidence intervals
mte = calculate_mte(rslt, data_frame, quantiles)
mte_up, mte_d = calculate_cof_int(rslt, init_dict, data_frame, mte, quantiles)
return quantiles, mte, mte_up, mte_d
def mte_and_cof_int_par(rslt, init_dict, data, college_years):
"""This function returns the parametric MTE divided by the number
of college years, which represents the returns per YEAR of
post-secondary schooling.
90 percent confidence intervals are computed analytically.
"""
# Define quantiles of u_D (unobserved resistance to treatment)
quantiles = [0.0001] + np.arange(0.01, 1.0, 0.01).tolist() + [0.9999]
# Calculate the MTE and confidence intervals
mte = calculate_mte(rslt, data, quantiles)
mte = [i / college_years for i in mte]
con_u, con_d = calculate_cof_int(rslt, init_dict, data, mte, quantiles)
return quantiles, mte, con_u, con_d
def plot_est_mte(rslt, init_dict, data_frame):
"""This function calculates the marginal treatment effect for different quartiles of the
unobservable V. ased on the calculation results."""
# Define the Quantiles and read in the original results
quantiles = [0.0001] + np.arange(0.01, 1.0, 0.01).tolist() + [0.9999]
mte_ = json.load(open("mte_original.json"))
mte_original = mte_[1]
mte_original_d = mte_[0]
mte_original_u = mte_[2]
# Calculate the MTE and confidence intervals
mte = calculate_mte(rslt, data_frame, quantiles)
mte = [i / 4 for i in mte]
mte_up, mte_d = calculate_cof_int(rslt, init_dict, data_frame, mte,
quantiles)
# Plot both curves
ax = plt.figure(figsize=(14, 6))
ax1 = ax.add_subplot(121)
ax1.set_ylabel(r"$B^{MTE}$")
ax1.set_xlabel("$u_D$")
(l1, ) = ax1.plot(quantiles, mte, color="blue")
ax1.plot(quantiles, mte_up, color="blue", linestyle=":")
ax1.plot(quantiles, mte_d, color="blue", linestyle=":")
ax1.set_ylim([-0.4, 0.5])
ax2 = ax.add_subplot(122)
ax2.set_ylabel(r"$B^{MTE}$")
ax2.set_xlabel("$u_D$")
(l4, ) = ax2.plot(quantiles, mte_original, color="orange")
ax2.plot(quantiles, mte_original_d, color="orange", linestyle=":")
ax2.plot(quantiles, mte_original_u, color="orange", linestyle=":")
ax2.set_ylim([-0.4, 0.5])
plt.legend([l1, l4], ["grmpy $B^{MTE}$", "original $B^{MTE}$"],
prop={"size": 18})
plt.tight_layout()
plt.savefig("fig-marginal-benefit-parametric-replication.png", dpi=300)
return mte
def plot_est_mte(rslt, init_dict, data_frame):
"""This function calculates the marginal treatment effect for different quartiles of the
unobservable V. ased on the calculation results."""
# Define the Quantiles and read in the original results
quantiles = [0.0001] + np.arange(0.01, 1., 0.01).tolist() + [0.9999]
mte_ = json.load(open('mte_original.json', 'r'))
mte_original = mte_[1]
mte_original_d = mte_[0]
mte_original_u = mte_[2]
# Calculate the MTE and confidence intervals
mte = calculate_mte(rslt, init_dict, data_frame, quantiles)
mte = [i / 4 for i in mte]
mte_up, mte_d = calculate_cof_int(rslt, init_dict, data_frame, mte,
quantiles)
# Plot both curveslscpu | grep MHz
ax = plt.figure().add_subplot(111)
ax.set_ylabel(r"$B^{MTE}$")
ax.set_xlabel("$u_S$")
ax.plot(quantiles, mte, label='grmpy MTE', color='blue')
ax.plot(quantiles, mte_up, color='blue', linestyle=':')
ax.plot(quantiles, mte_d, color='blue', linestyle=':')
ax.plot(quantiles, mte_original, label='original MTE', color='red')
ax.plot(quantiles, mte_original_d, color='red', linestyle=':')
ax.plot(quantiles, mte_original_u, color='red', linestyle=':')
ax.set_ylim([-0.4, 0.5])
plt.legend()
plt.tight_layout()
plt.savefig('fig-marginal-benefit-parametric-replication.png')
return mte