nkids0=x_df[ ['nkids_baseline'] ].values
#marital status at baseline
married0=x_df[ ['d_marital_2'] ].values
#age of child at baseline
agech0_a=x_df[['age_t0A']].values[:,0]
agech0_b=x_df[['age_t0B']].values[:,0]
d_childb=x_df[['d_childB']].values
d_childa=x_df[['d_childA']].values
"""
#Defines the instance with parameters
param0 = util.Parameters(alphap, alphaf, eta, gamma1, gamma2, gamma3, tfp,
sigma2theta, varphi, rho_theta_epsilon, rho_theta_ab,
wagep_betas, income_male_betas, marriagep_betas,
kidsp_betas, eitc_list, afdc_list, snap_list, cpi,
lambdas, kappas, pafdc, psnap, mup)
#For montercarlo integration
D = 20
#How many hours is part- and full-time work
hours_p = 15
hours_f = 40
hours = np.zeros(N)
childcare = np.zeros(N)
wr, cs, ws = 1, 1, 1
######################################################################
def elast_gen(bs, shocks):
eta = bs[0]
alphap = bs[1]
alphaf = bs[2]
#wage process
wagep_betas = np.array([bs[3], bs[4], bs[5], bs[6], bs[7]]).reshape((5, 1))
#Production function [young[cc0,cc1],old]
gamma1 = bs[8]
gamma2 = bs[9]
gamma3 = bs[10]
tfp = bs[11]
kappas = [[bs[12], bs[13], bs[14], bs[15]],
[bs[16], bs[17], bs[18], bs[19]]]
rho_theta_epsilon = bs[20]
lambdas = [1, 1]
#Re-defines the instance with parameters
param = util.Parameters(alphap, alphaf, eta, gamma1, gamma2, gamma3, tfp,
sigma2theta, rho_theta_epsilon, wagep_betas,
marriagep_betas, kidsp_betas, eitc_list, afdc_list,
snap_list, cpi, lambdas, kappas, pafdc, psnap, mup)
#The estimate class
output_ins = estimate.Estimate(nperiods, param, x_w, x_m, x_k, x_wmk,
passign, agech0, nkids0, married0, D,
dict_grid, M, N, moments_vector, var_cov,
hours_p, hours_f, wr, cs, ws)
hours = np.zeros(N)
childcare = np.zeros(N)
model_orig = util.Utility(param, N, x_w, x_m, x_k, passign, nkids0,
married0, hours, childcare, agech0, hours_p,
hours_f, wr, cs, ws)
#Obtaining emax instance: this is fixed throughout the exercise
emax_instance = output_ins.emax(param, model_orig)
choices_c = {}
models = []
for j in range(2):
np.save(
'/mnt/Research/nealresearch/new-hope-secure/newhopemount/results/Model/experiments/NH/shock.npy',
shocks[j])
models.append(
Shock(param, N, x_w, x_m, x_k, passign, nkids0, married0, hours,
childcare, agech0, hours_p, hours_f, wr, cs, ws))
choices_c['Choice_' + str(j)] = output_ins.samples(
param, emax_instance, models[j])
#Computing changes in % employment for control group
h_sim_matrix = []
employment = []
wages = []
full = []
for j in range(2):
h_sim_matrix.append(choices_c['Choice_' + str(j)]['hours_matrix'])
employment.append(choices_c['Choice_' + str(j)]['hours_matrix'] > 0)
full.append(choices_c['Choice_' + str(j)]['hours_matrix'] == hours_f)
wages.append(choices_c['Choice_' + str(j)]['wage_matrix'])
#Extensive margin
elast_extensive = np.zeros(M)
for j in range(M):
elast_periods = np.zeros(nperiods)
for t in range(nperiods):
elast_periods[t] = np.mean(
(employment[1][:, t, j] - employment[0][:, t, j]),
axis=0) / (shocks[1] * np.mean(
(employment[0][:, t, j]), axis=0))
elast_extensive[j] = np.mean(elast_periods)
#Intensive margin
elast_intensive = np.zeros(M)
for j in range(M):
elast_periods = np.zeros(nperiods)
for t in range(nperiods):
sample = (employment[0][:, t, j] == 1)
elast_periods[t] = np.mean(
(h_sim_matrix[1][sample, t, j] -
h_sim_matrix[0][sample, t, j]),
axis=0) / (shocks[1] *
np.mean(h_sim_matrix[0][sample, t, j], axis=0))
elast_intensive[j] = np.mean(elast_periods)
return {'Extensive': elast_extensive, 'Intensive': elast_intensive}
nkids0 = x_df[['nkids_baseline']].values
#marital status at baseline
married0 = x_df[['d_marital_2']].values
#age of child at baseline
agech0 = x_df[['age_t0']].values
age_child = np.zeros((N, nperiods))
for t in range(nperiods):
age_child = agech0 + t
#Defines the instance with parameters
param0 = util.Parameters(alphap, alphaf, mu_c, eta, gamma1, gamma2, gamma3,
tfp, sigma2theta, rho_theta_epsilon, wagep_betas,
income_male_betas, c_emp_spouse, marriagep_betas,
kidsp_betas, eitc_list, afdc_list, snap_list, cpi,
fpl_list, lambdas, kappas, pafdc, psnap, mup, sigma_z)
###Auxiliary estimates###
moments_vector = pd.read_csv(
"/home/jrodriguez/NH_HC/results/model_v2/aux_model/moments_vector.csv"
).values
#This is the var cov matrix of aux estimates
var_cov = pd.read_csv(
"/home/jrodriguez/NH_HC/results/model_v2/aux_model/var_cov.csv").values
#The vector of aux standard errors
#Using diagonal of Var-Cov matrix of simulated moments
se_vector = np.sqrt(np.diagonal(var_cov))
#Assuming random start
theta0 = np.exp(np.random.randn(N))
#number of kids at baseline
nkids0 = x_df[['nkids_baseline']].values
#marital status at baseline
married0 = x_df[['d_marital_2']].values
#age of child at baseline
agech0 = x_df[['age_t0']].values
#Defines the instance with parameters
param0 = util.Parameters(alphap, alphaf, eta, alpha_cc, gamma1, gamma2, tfp,
sigmatheta, wagep_betas, marriagep_betas, kidsp_betas,
eitc_list, afdc_list, snap_list, cpi, q, scalew,
shapew, lambdas, kappas, pafdc, psnap)
#For montercarlo integration
D = [5, 10, 15, 20, 25, 30, 35, 40, 50, 60, 70, 80, 90, 100]
#How many hours is part- and full-time work
hours_p = 15
hours_f = 40
hours = np.zeros(N)
childcare = np.zeros(N)
wr, cs, ws = 1, 1, 1
#This is an arbitrary initialization of Utility class
model = util.Utility(param0, N, x_w, x_m, x_k, passign, nkids0, married0,
