def calculateKYratioDifference(sim_wealth, weights, total_output, target_KY):
'''
Calculates the absolute distance between the simulated capital-to-output
ratio and the true U.S. level.
Parameters:
-------------
sim_wealth : numpy.array
Array with simulated wealth values.
weights : numpy.array
List of weights for each row of sim_wealth.
total_output : float
Denominator for the simulated K/Y ratio.
target_KY : float
Actual U.S. K/Y ratio to match.
Returns:
------------
distance : float
Absolute distance between simulated and actual K/Y ratios.
'''
sim_K = weightedAverageSimData(sim_wealth, weights)
sim_KY = sim_K / total_output
distance = (sim_KY - target_KY)**1.0
return distance
def calculateKYratioDifference(sim_wealth,weights,total_output,target_KY):
'''
Calculates the absolute distance between the simulated capital-to-output
ratio and the true U.S. level.
Parameters:
-------------
sim_wealth : numpy.array
Array with simulated wealth values.
weights : numpy.array
List of weights for each row of sim_wealth.
total_output : float
Denominator for the simulated K/Y ratio.
target_KY : float
Actual U.S. K/Y ratio to match.
Returns:
------------
distance : float
Absolute distance between simulated and actual K/Y ratios.
'''
sim_K = weightedAverageSimData(sim_wealth,weights)
sim_KY = sim_K/total_output
distance = (sim_KY - target_KY)**1.0
return distance
def calcKappaMean(beta,nabla):
'''
Calculates the average MPC for the given parameters. This is a very small
sub-function of makeCSTWresults().
'''
beta_list = makeUniformDiscreteDistribution(beta,nabla,N=Params.pref_type_count)
assignBetaDistribution(est_type_list,beta_list)
multiThreadCommandsFake(est_type_list,results_commands)
kappa_all = weightedAverageSimData(np.vstack((this_type.kappa_history for this_type in est_type_list)),np.tile(Params.age_weight_short/float(Params.pref_type_count),Params.pref_type_count))
return kappa_all
def calcKappaMean(beta, nabla):
'''
Calculates the average MPC for the given parameters. This is a very small
sub-function of makeCSTWresults().
'''
beta_list = makeUniformDiscreteDistribution(beta,
nabla,
N=Params.pref_type_count)
assignBetaDistribution(est_type_list, beta_list)
multiThreadCommandsFake(est_type_list, results_commands)
kappa_all = weightedAverageSimData(
np.vstack((this_type.kappa_history for this_type in est_type_list)),
np.tile(Params.age_weight_short / float(Params.pref_type_count),
Params.pref_type_count))
return kappa_all
def makeCSTWresults(beta, nabla, save_name=None):
'''
Produces a variety of results for the cstwMPC paper (usually after estimating).
'''
beta_list = makeUniformDiscreteDistribution(beta,
nabla,
N=Params.pref_type_count)
assignBetaDistribution(est_type_list, beta_list)
multiThreadCommandsFake(est_type_list, results_commands)
if Params.do_lifecycle: # This can probably be removed
sim_length = Params.total_T
else:
sim_length = Params.sim_periods
sim_wealth = (np.vstack(
(this_type.W_history for this_type in est_type_list))).flatten()
sim_wealth_short = (np.vstack((this_type.W_history[0:sim_length]
for this_type in est_type_list))).flatten()
sim_kappa = (np.vstack(
(this_type.kappa_history for this_type in est_type_list))).flatten()
sim_income = (np.vstack((this_type.Y_history[0:sim_length] *
np.asarray(this_type.temp_shocks[0:sim_length])
for this_type in est_type_list))).flatten()
sim_ratio = (np.vstack(
(this_type.W_history[0:sim_length] / this_type.Y_history[0:sim_length]
for this_type in est_type_list))).flatten()
if Params.do_lifecycle:
sim_unemp = (np.vstack((np.vstack(
(this_type.income_unemploy == np.asarray(
this_type.temp_shocks[0:Params.working_T]),
np.zeros((Params.retired_T, Params.sim_pop_size), dtype=bool)))
for this_type in est_type_list))).flatten()
sim_emp = (np.vstack((np.vstack(
(this_type.income_unemploy != np.asarray(
this_type.temp_shocks[0:Params.working_T]),
np.zeros((Params.retired_T, Params.sim_pop_size), dtype=bool)))
for this_type in est_type_list))).flatten()
sim_ret = (np.vstack((np.vstack(
(np.zeros((Params.working_T, Params.sim_pop_size), dtype=bool),
np.ones((Params.retired_T, Params.sim_pop_size), dtype=bool)))
for this_type in est_type_list))).flatten()
else:
sim_unemp = np.vstack((this_type.income_unemploy == np.asarray(
this_type.temp_shocks[0:sim_length])
for this_type in est_type_list)).flatten()
sim_emp = np.vstack((this_type.income_unemploy != np.asarray(
this_type.temp_shocks[0:sim_length])
for this_type in est_type_list)).flatten()
sim_ret = np.zeros(sim_emp.size, dtype=bool)
sim_weight_all = np.tile(
np.repeat(Params.age_weight_all, Params.sim_pop_size),
Params.pref_type_count)
sim_weight_short = np.tile(
np.repeat(Params.age_weight_short, Params.sim_pop_size),
Params.pref_type_count)
if Params.do_beta_dist and Params.do_lifecycle:
kappa_mean_by_age_type = (np.mean(np.vstack(
(this_type.kappa_history for this_type in est_type_list)),
axis=1)).reshape(
(Params.pref_type_count * 3,
DropoutType.T_total))
kappa_mean_by_age_pref = np.zeros(
(Params.pref_type_count, DropoutType.T_total)) + np.nan
for j in range(Params.pref_type_count):
kappa_mean_by_age_pref[
j, ] = Params.d_pct * kappa_mean_by_age_type[
3 * j + 0, ] + Params.h_pct * kappa_mean_by_age_type[
3 * j + 1, ] + Params.c_pct * kappa_mean_by_age_type[
3 * j + 2, ]
kappa_mean_by_age = np.mean(kappa_mean_by_age_pref, axis=0)
kappa_lo_beta_by_age = kappa_mean_by_age_pref[0, ]
kappa_hi_beta_by_age = kappa_mean_by_age_pref[Params.pref_type_count -
1, ]
lorenz_fig_data = makeLorenzFig(Params.SCF_wealth, Params.SCF_weights,
sim_wealth, sim_weight_all)
mpc_fig_data = makeMPCfig(sim_kappa, sim_weight_short)
kappa_all = weightedAverageSimData(
np.vstack((this_type.kappa_history for this_type in est_type_list)),
np.tile(Params.age_weight_short / float(Params.pref_type_count),
Params.pref_type_count))
kappa_unemp = np.sum(
sim_kappa[sim_unemp] * sim_weight_short[sim_unemp]) / np.sum(
sim_weight_short[sim_unemp])
kappa_emp = np.sum(
sim_kappa[sim_emp] * sim_weight_short[sim_emp]) / np.sum(
sim_weight_short[sim_emp])
kappa_ret = np.sum(
sim_kappa[sim_ret] * sim_weight_short[sim_ret]) / np.sum(
sim_weight_short[sim_ret])
my_cutoffs = [(0.99, 1), (0.9, 1), (0.8, 1), (0.6, 1), (0.5, 1), (0.4, 1),
(0.0, 0.5)]
kappa_by_ratio_groups = avgDataSlice(sim_kappa, sim_ratio, my_cutoffs,
sim_weight_short)
kappa_by_income_groups = avgDataSlice(sim_kappa, sim_income, my_cutoffs,
sim_weight_short)
quintile_points = extractPercentiles(sim_wealth_short,
weights=sim_weight_short,
percentiles=[0.2, 0.4, 0.6, 0.8])
wealth_quintiles = np.ones(sim_wealth_short.size, dtype=int)
wealth_quintiles[sim_wealth_short > quintile_points[0]] = 2
wealth_quintiles[sim_wealth_short > quintile_points[1]] = 3
wealth_quintiles[sim_wealth_short > quintile_points[2]] = 4
wealth_quintiles[sim_wealth_short > quintile_points[3]] = 5
MPC_cutoff = extractPercentiles(sim_kappa,
weights=sim_weight_short,
percentiles=[2.0 / 3.0])
these_quintiles = wealth_quintiles[sim_kappa > MPC_cutoff]
these_weights = sim_weight_short[sim_kappa > MPC_cutoff]
hand_to_mouth_total = np.sum(these_weights)
hand_to_mouth_pct = []
for q in range(5):
hand_to_mouth_pct.append(
np.sum(these_weights[these_quintiles == (q + 1)]) /
hand_to_mouth_total)
results_string = 'Estimate is beta=' + str(beta) + ', nabla=' + str(
nabla) + '\n'
results_string += 'Average MPC for all consumers is ' + mystr(
kappa_all) + '\n'
results_string += 'Average MPC in the top 1% of W/Y is ' + mystr(
kappa_by_ratio_groups[0]) + '\n'
results_string += 'Average MPC in the top 10% of W/Y is ' + mystr(
kappa_by_ratio_groups[1]) + '\n'
results_string += 'Average MPC in the top 20% of W/Y is ' + mystr(
kappa_by_ratio_groups[2]) + '\n'
results_string += 'Average MPC in the top 40% of W/Y is ' + mystr(
kappa_by_ratio_groups[3]) + '\n'
results_string += 'Average MPC in the top 50% of W/Y is ' + mystr(
kappa_by_ratio_groups[4]) + '\n'
results_string += 'Average MPC in the top 60% of W/Y is ' + mystr(
kappa_by_ratio_groups[5]) + '\n'
results_string += 'Average MPC in the bottom 50% of W/Y is ' + mystr(
kappa_by_ratio_groups[6]) + '\n'
results_string += 'Average MPC in the top 1% of y is ' + mystr(
kappa_by_income_groups[0]) + '\n'
results_string += 'Average MPC in the top 10% of y is ' + mystr(
kappa_by_income_groups[1]) + '\n'
results_string += 'Average MPC in the top 20% of y is ' + mystr(
kappa_by_income_groups[2]) + '\n'
results_string += 'Average MPC in the top 40% of y is ' + mystr(
kappa_by_income_groups[3]) + '\n'
results_string += 'Average MPC in the top 50% of y is ' + mystr(
kappa_by_income_groups[4]) + '\n'
results_string += 'Average MPC in the top 60% of y is ' + mystr(
kappa_by_income_groups[5]) + '\n'
results_string += 'Average MPC in the bottom 50% of y is ' + mystr(
kappa_by_income_groups[6]) + '\n'
results_string += 'Average MPC for the employed is ' + mystr(
kappa_emp) + '\n'
results_string += 'Average MPC for the unemployed is ' + mystr(
kappa_unemp) + '\n'
results_string += 'Average MPC for the retired is ' + mystr(
kappa_ret) + '\n'
results_string += 'Of the population with the 1/3 highest MPCs...' + '\n'
results_string += mystr(
hand_to_mouth_pct[0] *
100) + '% are in the bottom wealth quintile,' + '\n'
results_string += mystr(
hand_to_mouth_pct[1] *
100) + '% are in the second wealth quintile,' + '\n'
results_string += mystr(hand_to_mouth_pct[2] *
100) + '% are in the third wealth quintile,' + '\n'
results_string += mystr(
hand_to_mouth_pct[3] *
100) + '% are in the fourth wealth quintile,' + '\n'
results_string += 'and ' + mystr(
hand_to_mouth_pct[4] *
100) + '% are in the top wealth quintile.' + '\n'
print(results_string)
if save_name is not None:
with open('./Results/' + save_name + 'LorenzFig.txt', 'w') as f:
my_writer = csv.writer(
f,
delimiter='\t',
)
for j in range(len(lorenz_fig_data[0])):
my_writer.writerow([
lorenz_fig_data[0][j], lorenz_fig_data[1][j],
lorenz_fig_data[2][j]
])
f.close()
with open('./Results/' + save_name + 'MPCfig.txt', 'w') as f:
my_writer = csv.writer(f, delimiter='\t')
for j in range(len(mpc_fig_data[0])):
my_writer.writerow([lorenz_fig_data[0][j], mpc_fig_data[1][j]])
f.close()
if Params.do_beta_dist and Params.do_lifecycle:
with open('./Results/' + save_name + 'KappaByAge.txt', 'w') as f:
my_writer = csv.writer(f, delimiter='\t')
for j in range(len(kappa_mean_by_age)):
my_writer.writerow([
kappa_mean_by_age[j], kappa_lo_beta_by_age[j],
kappa_hi_beta_by_age[j]
])
f.close()
with open('./Results/' + save_name + 'Results.txt', 'w') as f:
f.write(results_string)
f.close()
def makeCSTWresults(beta,nabla,save_name=None):
'''
Produces a variety of results for the cstwMPC paper (usually after estimating).
'''
beta_list = makeUniformDiscreteDistribution(beta,nabla,N=Params.pref_type_count)
assignBetaDistribution(est_type_list,beta_list)
multiThreadCommandsFake(est_type_list,results_commands)
lorenz_distance = np.sqrt(betaDistObjective(nabla))
#lorenz_distance = 0.0
if Params.do_lifecycle: # This can probably be removed
sim_length = Params.total_T
else:
sim_length = Params.sim_periods
sim_wealth = (np.vstack((this_type.W_history for this_type in est_type_list))).flatten()
sim_wealth_short = (np.vstack((this_type.W_history[0:sim_length] for this_type in est_type_list))).flatten()
sim_kappa = (np.vstack((this_type.kappa_history for this_type in est_type_list))).flatten()
sim_income = (np.vstack((this_type.Y_history[0:sim_length]*np.asarray(this_type.temp_shocks[0:sim_length]) for this_type in est_type_list))).flatten()
sim_ratio = (np.vstack((this_type.W_history[0:sim_length]/this_type.Y_history[0:sim_length] for this_type in est_type_list))).flatten()
if Params.do_lifecycle:
sim_unemp = (np.vstack((np.vstack((this_type.income_unemploy == np.asarray(this_type.temp_shocks[0:Params.working_T]),np.zeros((Params.retired_T,Params.sim_pop_size),dtype=bool))) for this_type in est_type_list))).flatten()
sim_emp = (np.vstack((np.vstack((this_type.income_unemploy != np.asarray(this_type.temp_shocks[0:Params.working_T]),np.zeros((Params.retired_T,Params.sim_pop_size),dtype=bool))) for this_type in est_type_list))).flatten()
sim_ret = (np.vstack((np.vstack((np.zeros((Params.working_T,Params.sim_pop_size),dtype=bool),np.ones((Params.retired_T,Params.sim_pop_size),dtype=bool))) for this_type in est_type_list))).flatten()
else:
sim_unemp = np.vstack((this_type.income_unemploy == np.asarray(this_type.temp_shocks[0:sim_length]) for this_type in est_type_list)).flatten()
sim_emp = np.vstack((this_type.income_unemploy != np.asarray(this_type.temp_shocks[0:sim_length]) for this_type in est_type_list)).flatten()
sim_ret = np.zeros(sim_emp.size,dtype=bool)
sim_weight_all = np.tile(np.repeat(Params.age_weight_all,Params.sim_pop_size),Params.pref_type_count)
sim_weight_short = np.tile(np.repeat(Params.age_weight_short,Params.sim_pop_size),Params.pref_type_count)
if Params.do_beta_dist and Params.do_lifecycle:
kappa_mean_by_age_type = (np.mean(np.vstack((this_type.kappa_history for this_type in est_type_list)),axis=1)).reshape((Params.pref_type_count*3,DropoutType.T_total))
kappa_mean_by_age_pref = np.zeros((Params.pref_type_count,DropoutType.T_total)) + np.nan
for j in range(Params.pref_type_count):
kappa_mean_by_age_pref[j,] = Params.d_pct*kappa_mean_by_age_type[3*j+0,] + Params.h_pct*kappa_mean_by_age_type[3*j+1,] + Params.c_pct*kappa_mean_by_age_type[3*j+2,]
kappa_mean_by_age = np.mean(kappa_mean_by_age_pref,axis=0)
kappa_lo_beta_by_age = kappa_mean_by_age_pref[0,]
kappa_hi_beta_by_age = kappa_mean_by_age_pref[Params.pref_type_count-1,]
lorenz_fig_data = makeLorenzFig(Params.SCF_wealth,Params.SCF_weights,sim_wealth,sim_weight_all)
mpc_fig_data = makeMPCfig(sim_kappa,sim_weight_short)
kappa_all = weightedAverageSimData(np.vstack((this_type.kappa_history for this_type in est_type_list)),np.tile(Params.age_weight_short/float(Params.pref_type_count),Params.pref_type_count))
kappa_unemp = np.sum(sim_kappa[sim_unemp]*sim_weight_short[sim_unemp])/np.sum(sim_weight_short[sim_unemp])
kappa_emp = np.sum(sim_kappa[sim_emp]*sim_weight_short[sim_emp])/np.sum(sim_weight_short[sim_emp])
kappa_ret = np.sum(sim_kappa[sim_ret]*sim_weight_short[sim_ret])/np.sum(sim_weight_short[sim_ret])
my_cutoffs = [(0.99,1),(0.9,1),(0.8,1),(0.6,0.8),(0.4,0.6),(0.2,0.4),(0.0,0.2)]
kappa_by_ratio_groups = avgDataSlice(sim_kappa,sim_ratio,my_cutoffs,sim_weight_short)
kappa_by_income_groups = avgDataSlice(sim_kappa,sim_income,my_cutoffs,sim_weight_short)
quintile_points = extractPercentiles(sim_wealth_short,weights=sim_weight_short,percentiles=[0.2, 0.4, 0.6, 0.8])
wealth_quintiles = np.ones(sim_wealth_short.size,dtype=int)
wealth_quintiles[sim_wealth_short > quintile_points[0]] = 2
wealth_quintiles[sim_wealth_short > quintile_points[1]] = 3
wealth_quintiles[sim_wealth_short > quintile_points[2]] = 4
wealth_quintiles[sim_wealth_short > quintile_points[3]] = 5
MPC_cutoff = extractPercentiles(sim_kappa,weights=sim_weight_short,percentiles=[2.0/3.0])
these_quintiles = wealth_quintiles[sim_kappa > MPC_cutoff]
these_weights = sim_weight_short[sim_kappa > MPC_cutoff]
hand_to_mouth_total = np.sum(these_weights)
hand_to_mouth_pct = []
for q in range(5):
hand_to_mouth_pct.append(np.sum(these_weights[these_quintiles == (q+1)])/hand_to_mouth_total)
results_string = 'Estimate is beta=' + str(beta) + ', nabla=' + str(nabla) + '\n'
results_string += 'Lorenz distance is ' + str(lorenz_distance) + '\n'
results_string += 'Average MPC for all consumers is ' + mystr(kappa_all) + '\n'
results_string += 'Average MPC in the top percentile of W/Y is ' + mystr(kappa_by_ratio_groups[0]) + '\n'
results_string += 'Average MPC in the top decile of W/Y is ' + mystr(kappa_by_ratio_groups[1]) + '\n'
results_string += 'Average MPC in the top quintile of W/Y is ' + mystr(kappa_by_ratio_groups[2]) + '\n'
results_string += 'Average MPC in the second quintile of W/Y is ' + mystr(kappa_by_ratio_groups[3]) + '\n'
results_string += 'Average MPC in the middle quintile of W/Y is ' + mystr(kappa_by_ratio_groups[4]) + '\n'
results_string += 'Average MPC in the fourth quintile of W/Y is ' + mystr(kappa_by_ratio_groups[5]) + '\n'
results_string += 'Average MPC in the bottom quintile of W/Y is ' + mystr(kappa_by_ratio_groups[6]) + '\n'
results_string += 'Average MPC in the top percentile of y is ' + mystr(kappa_by_income_groups[0]) + '\n'
results_string += 'Average MPC in the top decile of y is ' + mystr(kappa_by_income_groups[1]) + '\n'
results_string += 'Average MPC in the top quintile of y is ' + mystr(kappa_by_income_groups[2]) + '\n'
results_string += 'Average MPC in the second quintile of y is ' + mystr(kappa_by_income_groups[3]) + '\n'
results_string += 'Average MPC in the middle quintile of y is ' + mystr(kappa_by_income_groups[4]) + '\n'
results_string += 'Average MPC in the fourth quintile of y is ' + mystr(kappa_by_income_groups[5]) + '\n'
results_string += 'Average MPC in the bottom quintile of y is ' + mystr(kappa_by_income_groups[6]) + '\n'
results_string += 'Average MPC for the employed is ' + mystr(kappa_emp) + '\n'
results_string += 'Average MPC for the unemployed is ' + mystr(kappa_unemp) + '\n'
results_string += 'Average MPC for the retired is ' + mystr(kappa_ret) + '\n'
results_string += 'Of the population with the 1/3 highest MPCs...' + '\n'
results_string += mystr(hand_to_mouth_pct[0]*100) + '% are in the bottom wealth quintile,' + '\n'
results_string += mystr(hand_to_mouth_pct[1]*100) + '% are in the second wealth quintile,' + '\n'
results_string += mystr(hand_to_mouth_pct[2]*100) + '% are in the third wealth quintile,' + '\n'
results_string += mystr(hand_to_mouth_pct[3]*100) + '% are in the fourth wealth quintile,' + '\n'
results_string += 'and ' + mystr(hand_to_mouth_pct[4]*100) + '% are in the top wealth quintile.' + '\n'
print(results_string)
if save_name is not None:
with open('./Results/' + save_name + 'LorenzFig.txt','w') as f:
my_writer = csv.writer(f, delimiter='\t',)
for j in range(len(lorenz_fig_data[0])):
my_writer.writerow([lorenz_fig_data[0][j], lorenz_fig_data[1][j], lorenz_fig_data[2][j]])
f.close()
with open('./Results/' + save_name + 'MPCfig.txt','w') as f:
my_writer = csv.writer(f, delimiter='\t')
for j in range(len(mpc_fig_data[0])):
my_writer.writerow([lorenz_fig_data[0][j], mpc_fig_data[1][j]])
f.close()
if Params.do_beta_dist and Params.do_lifecycle:
with open('./Results/' + save_name + 'KappaByAge.txt','w') as f:
my_writer = csv.writer(f, delimiter='\t')
for j in range(len(kappa_mean_by_age)):
my_writer.writerow([kappa_mean_by_age[j], kappa_lo_beta_by_age[j], kappa_hi_beta_by_age[j]])
f.close()
with open('./Results/' + save_name + 'Results.txt','w') as f:
f.write(results_string)
f.close()
