def calcKappaMean(DiscFac, nabla):
'''
Calculates the average MPC for the given parameters. This is a very small
sub-function of sensitivityAnalysis.
Parameters
----------
DiscFac : float
Center of the uniform distribution of discount factors
nabla : float
Width of the uniform distribution of discount factors
Returns
-------
kappa_all : float
Average marginal propensity to consume in the population.
'''
DiscFac_list = approxUniform(N=Params.pref_type_count,
bot=DiscFac - nabla,
top=DiscFac + nabla)[1]
assignBetaDistribution(est_type_list, DiscFac_list)
multiThreadCommandsFake(est_type_list, beta_point_commands)
kappa_all = calcWeightedAvg(
np.vstack((this_type.kappa_history for this_type in est_type_list)),
np.tile(Params.age_weight_all / float(Params.pref_type_count),
Params.pref_type_count))
return kappa_all
def calcKappaMean(DiscFac,nabla):
'''
Calculates the average MPC for the given parameters. This is a very small
sub-function of sensitivityAnalysis.
Parameters
----------
DiscFac : float
Center of the uniform distribution of discount factors
nabla : float
Width of the uniform distribution of discount factors
Returns
-------
kappa_all : float
Average marginal propensity to consume in the population.
'''
DiscFac_list = approxUniform(N=Params.pref_type_count,bot=DiscFac-nabla,top=DiscFac+nabla)[1]
assignBetaDistribution(est_type_list,DiscFac_list)
multiThreadCommandsFake(est_type_list,beta_point_commands)
kappa_all = calcWeightedAvg(np.vstack((this_type.kappa_history for this_type in est_type_list)),
np.tile(Params.age_weight_all/float(Params.pref_type_count),
Params.pref_type_count))
return kappa_all
def makeCSTWresults(DiscFac, nabla, save_name=None):
'''
Produces a variety of results for the cstwMPC paper (usually after estimating).
Parameters
----------
DiscFac : float
Center of the uniform distribution of discount factors
nabla : float
Width of the uniform distribution of discount factors
save_name : string
Name to save the calculated results, for later use in producing figures
and tables, etc.
Returns
-------
none
'''
DiscFac_list = approxUniform(N=Params.pref_type_count,
bot=DiscFac - nabla,
top=DiscFac + nabla)[1]
assignBetaDistribution(est_type_list, DiscFac_list)
multiThreadCommandsFake(est_type_list, beta_point_commands)
lorenz_distance = np.sqrt(betaDistObjective(nabla))
makeCSTWstats(DiscFac, nabla, est_type_list, Params.age_weight_all,
lorenz_distance, save_name)
def makeCSTWresults(DiscFac,nabla,save_name=None):
'''
Produces a variety of results for the cstwMPC paper (usually after estimating).
Parameters
----------
DiscFac : float
Center of the uniform distribution of discount factors
nabla : float
Width of the uniform distribution of discount factors
save_name : string
Name to save the calculated results, for later use in producing figures
and tables, etc.
Returns
-------
none
'''
DiscFac_list = approxUniform(N=Params.pref_type_count,bot=DiscFac-nabla,top=DiscFac+nabla)[1]
assignBetaDistribution(est_type_list,DiscFac_list)
multiThreadCommandsFake(est_type_list,beta_point_commands)
lorenz_distance = np.sqrt(betaDistObjective(nabla))
makeCSTWstats(DiscFac,nabla,est_type_list,Params.age_weight_all,lorenz_distance,save_name)
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 simulateKYratioDifference(beta,nabla,N,type_list,weights,total_output,target):
'''
Assigns a uniform distribution over beta with width 2*nabla and N points, then
solves and simulates all agent types in type_list and compares the simuated
K/Y ratio to the target K/Y ratio.
'''
if type(beta) in (list,np.ndarray,np.array):
beta = beta[0]
beta_list = makeUniformDiscreteDistribution(beta,nabla,N)
assignBetaDistribution(type_list,beta_list)
multiThreadCommandsFake(type_list,beta_point_commands)
my_diff = calculateKYratioDifference(np.vstack((this_type.W_history for this_type in type_list)),np.tile(weights/float(N),N),total_output,target)
#print('Tried beta=' + str(beta) + ', nabla=' + str(nabla) + ', got diff=' + str(my_diff))
return my_diff
def simulateKYratioDifference(DiscFac, nabla, N, type_list, weights,
total_output, target):
'''
Assigns a uniform distribution over DiscFac with width 2*nabla and N points, then
solves and simulates all agent types in type_list and compares the simuated
K/Y ratio to the target K/Y ratio.
Parameters
----------
DiscFac : float
Center of the uniform distribution of discount factors.
nabla : float
Width of the uniform distribution of discount factors.
N : int
Number of discrete consumer types.
type_list : [cstwMPCagent]
List of agent types to solve and simulate after assigning discount factors.
weights : np.array
Age-conditional array of population weights.
total_output : float
Total output of the economy, denominator for the K/Y calculation.
target : float
Target level of capital-to-output ratio.
Returns
-------
my_diff : float
Difference between simulated and target capital-to-output ratios.
'''
if type(DiscFac) in (list, np.ndarray, np.array):
DiscFac = DiscFac[0]
DiscFac_list = approxUniform(N, DiscFac - nabla, DiscFac +
nabla)[1] # only take values, not probs
assignBetaDistribution(type_list, DiscFac_list)
multiThreadCommandsFake(type_list, beta_point_commands)
my_diff = calculateKYratioDifference(
np.vstack((this_type.W_history for this_type in type_list)),
np.tile(weights / float(N), N), total_output, target)
return my_diff
def simulateKYratioDifference(DiscFac,nabla,N,type_list,weights,total_output,target):
'''
Assigns a uniform distribution over DiscFac with width 2*nabla and N points, then
solves and simulates all agent types in type_list and compares the simuated
K/Y ratio to the target K/Y ratio.
Parameters
----------
DiscFac : float
Center of the uniform distribution of discount factors.
nabla : float
Width of the uniform distribution of discount factors.
N : int
Number of discrete consumer types.
type_list : [cstwMPCagent]
List of agent types to solve and simulate after assigning discount factors.
weights : np.array
Age-conditional array of population weights.
total_output : float
Total output of the economy, denominator for the K/Y calculation.
target : float
Target level of capital-to-output ratio.
Returns
-------
my_diff : float
Difference between simulated and target capital-to-output ratios.
'''
if type(DiscFac) in (list,np.ndarray,np.array):
DiscFac = DiscFac[0]
DiscFac_list = approxUniform(N,DiscFac-nabla,DiscFac+nabla)[1] # only take values, not probs
assignBetaDistribution(type_list,DiscFac_list)
multiThreadCommandsFake(type_list,beta_point_commands)
my_diff = calculateKYratioDifference(np.vstack((this_type.W_history for this_type in type_list)),
np.tile(weights/float(N),N),total_output,target)
return my_diff
def makeValidationFigures(params, use_cohorts):
'''
Make several figures that compare simulated outcomes from the estimated model
to their data counterparts, for external validation.
Parameters
----------
params : np.array
Size 33 array of model parameters, like that used for estimation.
use_cohorts : bool
Indicator for whether or not to model differences across cohorts.
Returns
-------
None
'''
# Make, solve, and simulate the types
param_dict = convertVecToDict(params)
if use_cohorts:
type_list = makeMultiTypeWithCohorts(param_dict)
else:
type_list = makeMultiTypeSimple(param_dict)
for this_type in type_list:
this_type.track_vars.append('MedLvlNow')
this_type.track_vars.append('iLvlNow')
this_type.track_vars.append('HitCfloor')
this_type.CalcExpectationFuncs = True
this_type.DeleteSolution = False
multiThreadCommandsFake(type_list, ['estimationAction()'], num_jobs=5)
# Combine simulated data across all types
aLvlHist = np.concatenate(
[this_type.aLvlNow_hist for this_type in type_list], axis=1)
hLvlHist = np.concatenate(
[this_type.hLvlNow_hist for this_type in type_list], axis=1)
OOPhist = np.concatenate(
[this_type.OOPmedNow_hist for this_type in type_list], axis=1)
MortHist = np.concatenate(
[this_type.DiePrbNow_hist for this_type in type_list], axis=1)
WeightHist = np.concatenate(
[this_type.CumLivPrb_hist for this_type in type_list], axis=1)
MedHist = np.concatenate(
[this_type.MedLvlNow_hist for this_type in type_list], axis=1)
# Combine data labels across types
HealthTert = np.concatenate(
[this_type.HealthTert for this_type in type_list])
HealthQuint = np.concatenate(
[this_type.HealthQuint for this_type in type_list])
WealthQuint = np.concatenate(
[this_type.WealthQuint for this_type in type_list])
IncQuint = np.concatenate(
[this_type.IncQuintLong for this_type in type_list])
Sex = np.concatenate([this_type.SexLong for this_type in type_list])
# Combine in-data-span masking array across all types
Active = hLvlHist > 0.
InDataSpan = np.concatenate(
[this_type.InDataSpanArray for this_type in type_list], axis=1)
WeightAdj = InDataSpan * WeightHist
# For each type, calculate the probability that no health investment is purchased at each age
# and the probability the
iLvlZeroRate = np.zeros((10, 25))
HitCfloorRate = np.zeros((10, 25))
for j in range(10):
this_type = type_list[j]
iLvlZero = this_type.iLvlNow_hist == 0.
HitCfloor = this_type.HitCfloor_hist == 1.
iLvlZeroSum = np.sum(iLvlZero * this_type.CumLivPrb_hist, axis=1)
HitCfloorSum = np.sum(HitCfloor * this_type.CumLivPrb_hist, axis=1)
PopSum = np.sum(this_type.CumLivPrb_hist, axis=1)
iLvlZeroRate[j, :] = iLvlZeroSum / PopSum
HitCfloorRate[j, :] = HitCfloorSum / PopSum
# Calculate median (pseudo) bank balances for each type
bLvl_init_median = np.zeros(10)
for n in range(10):
bLvl_init_median[n] = np.median(
type_list[n].aLvlInit) + type_list[n].IncomeNow[2]
# Extract deciles of health by age from the simulated data
pctiles = [0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9]
SimHealthPctiles = np.zeros((15, len(pctiles)))
for t in range(15):
SimHealthPctiles[t, :] = getPercentiles(hLvlHist[t, :],
weights=WeightAdj[t, :],
percentiles=pctiles)
# Plot the probability of purchasing zero health investment by age, sex, and income
colors = ['b', 'r', 'g', 'c', 'm']
AgeVec = np.linspace(67., 95., num=15)
for n in range(5):
plt.plot(AgeVec, iLvlZeroRate[n, :15], '-' + colors[n])
plt.xlabel('Age')
plt.ylabel(r'Prob[$n_{it}=0$]')
plt.title('Probability of Buying No Health Investment, Women')
plt.legend([
'Bottom quintile', 'Second quintile', 'Third quintile',
'Fourth quintile', 'Top quintile'
])
plt.savefig('../Figures/ZeroInvstWomen.pdf')
plt.show()
for n in range(5):
plt.plot(AgeVec, iLvlZeroRate[n + 5, :15], '-' + colors[n])
plt.xlabel('Age')
plt.ylabel(r'Prob[$n_{it}=0$]')
plt.title('Probability of Buying No Health Investment, Men')
plt.savefig('../Figures/ZeroInvstMen.pdf')
plt.show()
# Plot the probability of hitting the consumption floor by age, sex, and income
colors = ['b', 'r', 'g', 'c', 'm']
AgeVec = np.linspace(67., 95., num=15)
for n in range(5):
plt.plot(AgeVec, HitCfloorRate[n, :15], '-' + colors[n])
plt.xlabel('Age')
plt.ylabel(r'Prob[$c_{it}={c}$]')
plt.title('Probability of Using Consumption Floor, Women')
plt.legend([
'Bottom quintile', 'Second quintile', 'Third quintile',
'Fourth quintile', 'Top quintile'
])
plt.savefig('../Figures/cFloorWomen.pdf')
plt.show()
for n in range(5):
plt.plot(AgeVec, HitCfloorRate[n + 5, :15], '-' + colors[n])
plt.xlabel('Age')
plt.ylabel(r'Prob[$c_{it}={c}$]')
plt.title('Probability of Using Consumption Floor, Men')
plt.savefig('../Figures/cFloorMen.pdf')
plt.show()
# Plot health investment as a function of market resources by type, holding h and Dev fixed
B = np.linspace(1., 50., 201)
some_ones = np.ones_like(B)
hLvl = 0.6
Dev = 0.0
t = 0
Age = str(65 + 2 * t)
for i in range(5):
this_type = type_list[i]
MedShk = np.exp(this_type.MedShkMeanFunc[t](hLvl) +
Dev * this_type.MedShkStdFunc(hLvl))
I = np.maximum(
this_type.solution[t].PolicyFunc.iFunc(B, hLvl * some_ones,
MedShk * some_ones), 0.0)
plt.plot(B, I, '-' + colors[i])
plt.xlabel(r'Bank balances $b_{it}$, \$10,000 (y2000)')
plt.ylabel(r'Health investment $n_{it}$, \$10,000 (y2000)')
plt.xlim([1., 50.])
plt.ylim([-0.01, 0.65])
#plt.legend(['Bottom quintile','Second quintile','Third quintile','Fourth quintile','Top quintile'])
plt.title('Health Investment Function at Age ' + Age + ' by Income, Women')
plt.savefig('../Figures/iFuncWomen.pdf')
plt.show()
for i in range(5):
this_type = type_list[i + 5]
MedShk = np.exp(this_type.MedShkMeanFunc[t](hLvl) +
Dev * this_type.MedShkStdFunc(hLvl))
I = np.maximum(
this_type.solution[t].PolicyFunc.iFunc(B, hLvl * some_ones,
MedShk * some_ones), 0.0)
plt.plot(B, I, '-' + colors[i])
plt.xlabel(r'Bank balances $b_{it}$, \$10,000 (y2000)')
plt.ylabel(r'Health investment $n_{it}$, \$10,000 (y2000)')
plt.xlim([1., 50.])
plt.ylim([-0.01, 0.65])
plt.legend([
'Bottom quintile', 'Second quintile', 'Third quintile',
'Fourth quintile', 'Top quintile'
],
loc=4)
plt.title('Health Investment Function at Age ' + Age + ' by Income, Men')
plt.savefig('../Figures/iFuncMen.pdf')
plt.show()
# Plot PDV of total medical expenses by health at median wealth at age 69-70 by income quintile and sex
t = 2
H = np.linspace(0.0, 1.0, 201)
for n in range(5):
B = bLvl_init_median[n] * np.ones_like(H)
M = type_list[n].solution[t].TotalMedPDVfunc(B, H)
plt.plot(H, M, color=colors[n])
plt.xlim([0., 1.])
plt.ylim([0., 17])
plt.xlabel(r'Health capital $h_{it}$')
plt.ylabel('PDV total medical care, $10,000 (y2000)')
plt.legend([
'Bottom quintile', 'Second quintile', 'Third quintile',
'Fourth quintile', 'Top quintile'
])
plt.title('Total Medical Expenses by Health and Income, Women')
plt.savefig('../Figures/TotalMedPDVbyIncomeWomen.pdf')
plt.show()
for n in range(5, 10):
B = bLvl_init_median[n] * np.ones_like(H)
M = type_list[n].solution[t].TotalMedPDVfunc(B, H)
plt.plot(H, M, color=colors[n - 5])
plt.xlim([0., 1.])
plt.ylim([0., 17])
plt.xlabel(r'Health capital $h_{it}$')
plt.ylabel('PDV total medical care, $10,000 (y2000)')
#plt.legend(['Bottom quintile','Second quintile','Third quintile','Fourth quintile','Top quintile'])
plt.title('Total Medical Expenses by Health and Income, Men')
plt.savefig('../Figures/TotalMedPDVbyIncomeMen.pdf')
plt.show()
# Plot PDV of OOP medical expenses by health at median wealth at age 69-70 by income quintile and sex
colors = ['b', 'r', 'g', 'c', 'm']
t = 2
H = np.linspace(0.0, 1.0, 201)
for n in range(5):
B = bLvl_init_median[n] * np.ones_like(H)
M = type_list[n].solution[t].OOPmedPDVfunc(B, H)
plt.plot(H, M, color=colors[n])
plt.xlim([0., 1.])
plt.ylim([0., 3.5])
plt.xlabel(r'Health capital $h_{it}$')
plt.ylabel('PDV OOP medical expenses, $10,000 (y2000)')
#plt.legend(['Bottom quintile','Second quintile','Third quintile','Fourth quintile','Top quintile'])
plt.title('OOP Medical Expenses by Health and Income, Women')
plt.savefig('../Figures/OOPmedPDVbyIncomeWomen.pdf')
plt.show()
for n in range(5, 10):
B = bLvl_init_median[n] * np.ones_like(H)
M = type_list[n].solution[t].OOPmedPDVfunc(B, H)
plt.plot(H, M, color=colors[n - 5])
plt.xlim([0., 1.])
plt.ylim([0., 3.5])
plt.xlabel(r'Health capital $h_{it}$')
plt.ylabel('PDV total medical care, $10,000 (y2000)')
#plt.legend(['Bottom quintile','Second quintile','Third quintile','Fourth quintile','Top quintile'])
plt.title('OOP Medical Expenses by Health and Income, Men')
plt.savefig('../Figures/OOPmedPDVbyIncomeMen.pdf')
plt.show()
# Plot life expectancy by health at median wealth at age 69-70 by income quintile and sex
colors = ['b', 'r', 'g', 'c', 'm']
t = 2
H = np.linspace(0.0, 1.0, 201)
for n in range(5):
B = bLvl_init_median[n] * np.ones_like(H)
M = type_list[n].solution[t].ExpectedLifeFunc(B, H)
plt.plot(H, M, color=colors[n])
plt.xlim([0., 1.])
plt.ylim([0., 20.])
plt.xlabel(r'Health capital $h_{it}$')
plt.ylabel('Remaining years of life expectancy')
plt.legend([
'Bottom quintile', 'Second quintile', 'Third quintile',
'Fourth quintile', 'Top quintile'
])
plt.title('Life Expectancy at Age 69 by Health and Income, Women')
plt.savefig('../Figures/LifeExpectancybyIncomeWomen.pdf')
plt.show()
for n in range(5, 10):
B = bLvl_init_median[n] * np.ones_like(H)
M = type_list[n].solution[t].ExpectedLifeFunc(B, H)
plt.plot(H, M, color=colors[n - 5])
plt.xlim([0., 1.])
plt.ylim([0., 20.])
plt.xlabel(r'Health capital $h_{it}$')
plt.ylabel('Remaining years of life expectancy')
plt.legend([
'Bottom quintile', 'Second quintile', 'Third quintile',
'Fourth quintile', 'Top quintile'
])
plt.title('Life Expectancy at Age 69 by Health and Income, Men')
plt.savefig('../Figures/LifeExpectancybyIncomeMen.pdf')
plt.show()
# Extract deciles of health from the HRS data
DataHealthPctiles = np.zeros((15, len(pctiles)))
for t in range(15):
these = np.logical_and(Data.AgeBoolArray[:, :, t], Data.Alive)
h_temp = Data.h_data[these]
DataHealthPctiles[t, :] = getPercentiles(h_temp, percentiles=pctiles)
# Plot deciles of health by by age
plt.plot(AgeVec, SimHealthPctiles, '-k')
plt.plot(AgeVec, DataHealthPctiles, '--k')
plt.ylim(0., 1.)
plt.ylabel('Health capital $h_{it}$')
plt.xlabel('Age')
plt.title('Simulated vs Actual Distribution of Health by Age')
plt.savefig('../Figures/HealthDistribution.pdf')
plt.show()
OOPmodFunc = lambda x: np.log(10000 * x)
# Extract many percentiles of OOP spending from the simulated data
OOP_sim = OOPhist.flatten()
Weight_temp = WeightAdj.flatten()
CDFvalsSim = np.linspace(0.0001, 0.999, 1000)
OOPsimCDF_A0 = getPercentiles(OOP_sim * 10000,
weights=Weight_temp,
percentiles=CDFvalsSim)
OOPsimCDF_B0 = getPercentiles(OOPmodFunc(OOP_sim),
weights=Weight_temp,
percentiles=CDFvalsSim)
# Extract some percentiles of OOP spending from the HRS data
these = np.logical_and(Data.Alive, np.logical_not(np.isnan(Data.m_data)))
OOP_data = Data.m_data[these]
CDFvalsData = np.linspace(0.0001, 0.999, 500)
OOPdataCDF_A0 = getPercentiles(OOP_data * 10000,
weights=None,
percentiles=CDFvalsData)
OOPdataCDF_B0 = getPercentiles(OOPmodFunc(OOP_data),
weights=None,
percentiles=CDFvalsData)
# Plot the CDF of log out-of-pocket medical spending
plt.subplot(211)
plt.title('CDF of OOP Medical Spending')
plt.plot(OOPdataCDF_B0, CDFvalsData, '-r')
plt.plot(OOPsimCDF_B0, CDFvalsSim, '-b')
plt.xlim(8., 11.5)
plt.ylim(0.85, 1.0)
plt.xticks([
np.log(3000),
np.log(6000),
np.log(12000),
np.log(24000),
np.log(48000),
np.log(96000)
], ['3000', '6000', '12000', '24000', '48000', '96000'])
# Plot the CDF of out-of-pocket medical spending
plt.subplot(212)
plt.plot(OOPdataCDF_A0, CDFvalsData, '-r')
plt.plot(OOPsimCDF_A0, CDFvalsSim, '-b')
plt.xlim(0., 3000.)
plt.ylim(0.0, 0.9)
plt.xlabel('Out-of-pocket medical expenses, biannual')
plt.ylabel('Cumulative distribution')
plt.legend(['HRS data', 'Model'], loc=4)
plt.savefig('../Figures/OOPdistribution.pdf')
plt.show()
# Calculate the serial correlation of log OOP medical spending in simulated data
Med_sim = np.log(10000 * OOPhist + 1.)
serial_corr_sim = np.zeros(15)
serial_corr_sim_inc = np.zeros((15, 5))
for t in range(15):
these = np.logical_and(WeightAdj[t + 1, :] > 0., WeightAdj[t + 1, :] <
1.) # Alive but not the first simulated period
Med_t = Med_sim[t + 1, these]
Med_tm1 = Med_sim[t, these]
weight_reg = WeightAdj[t + 1, these]
const_reg = np.ones_like(Med_t)
regressors = np.transpose(np.vstack([const_reg, Med_tm1]))
temp_model = WLS(Med_t, regressors, weights=weight_reg)
temp_results = temp_model.fit()
serial_corr_sim[t] = temp_results.rsquared
for i in range(5):
those = np.logical_and(these, IncQuint == i + 1)
Med_t = Med_sim[t + 1, those]
Med_tm1 = Med_sim[t, those]
weight_reg = WeightAdj[t + 1, those]
const_reg = np.ones_like(Med_t)
regressors = np.transpose(np.vstack([const_reg, Med_tm1]))
temp_model = WLS(Med_t, regressors, weights=weight_reg)
temp_results = temp_model.fit()
serial_corr_sim_inc[t, i] = temp_results.rsquared
# Calculate the serial correlation of log OOP medical spending in HRS data
DataExists = np.logical_and(np.logical_not(np.isnan(Data.m_data[:-1, :])),
np.logical_not(np.isnan(Data.m_data[1:, :])))
BothAlive = np.logical_and(Data.Alive[:-1, :], Data.Alive[1:, :])
Usable = np.logical_and(DataExists, BothAlive)
serial_corr_data = np.zeros(15)
serial_corr_data_inc = np.zeros((15, 5))
Med_data = np.log(10000 * Data.m_data + 1.)
for t in range(15):
these = np.logical_and(Usable, Data.AgeBoolArray[:-1, :, t])
Med_t = Med_data[1:, :][these]
Med_tm1 = Med_data[:-1, :][these]
const_reg = np.ones_like(Med_t)
regressors = np.transpose(np.vstack([const_reg, Med_tm1]))
temp_model = OLS(Med_t, regressors)
temp_results = temp_model.fit()
serial_corr_data[t] = temp_results.rsquared
for i in range(5):
those = np.logical_and(these, Data.IncQuintBoolArray[:-1, :, i])
Med_t = Med_data[1:, :][those]
Med_tm1 = Med_data[:-1, :][those]
const_reg = np.ones_like(Med_t)
regressors = np.transpose(np.vstack([const_reg, Med_tm1]))
temp_model = OLS(Med_t, regressors)
temp_results = temp_model.fit()
serial_corr_data_inc[t, i] = temp_results.rsquared
# Make a plot of serial correlation of OOP medical expenses
plt.subplot(3, 2, 1)
plt.plot(AgeVec, serial_corr_data, '-r')
plt.plot(AgeVec, serial_corr_sim, '-b')
plt.ylim(0, 0.5)
plt.xticks([])
plt.text(75, 0.4, 'All individuals')
plt.subplot(3, 2, 2)
plt.plot(AgeVec, serial_corr_data_inc[:, 0], '-r')
plt.plot(AgeVec, serial_corr_sim_inc[:, 0], '-b')
plt.ylim(0, 0.5)
plt.xticks([])
plt.yticks([])
plt.text(70, 0.4, 'Bottom income quintile')
plt.subplot(3, 2, 3)
plt.plot(AgeVec, serial_corr_data_inc[:, 1], '-r')
plt.plot(AgeVec, serial_corr_sim_inc[:, 1], '-b')
plt.ylim(0, 0.5)
plt.xticks([])
plt.text(67, 0.4, 'Second income quintile')
plt.ylabel('$R^2$ of regression of $\log(OOP_{t})$ on $\log(OOP_{t-1})$')
plt.subplot(3, 2, 4)
plt.plot(AgeVec, serial_corr_data_inc[:, 2], '-r')
plt.plot(AgeVec, serial_corr_sim_inc[:, 2], '-b')
plt.ylim(0, 0.5)
plt.xticks([])
plt.yticks([])
plt.text(70, 0.4, 'Third income quintile')
plt.subplot(3, 2, 5)
plt.plot(AgeVec, serial_corr_data_inc[:, 3], '-r')
plt.plot(AgeVec, serial_corr_sim_inc[:, 3], '-b')
plt.ylim(0, 0.5)
plt.xlabel('Age')
plt.text(70, 0.4, 'Fourth income quintile')
plt.subplot(3, 2, 6)
plt.plot(AgeVec, serial_corr_data_inc[:, 4], '-r')
plt.plot(AgeVec, serial_corr_sim_inc[:, 4], '-b')
plt.ylim(0, 0.5)
plt.xlabel('Age')
plt.yticks([])
plt.text(70, 0.4, 'Top income quintile')
plt.savefig('../Figures/SerialCorrOOP.pdf')
plt.show()
# Make a plot of serial correlation of OOP medical expenses
plt.plot(AgeVec + 2, serial_corr_data, '-r')
plt.plot(AgeVec + 2, serial_corr_sim, '-b')
plt.xlabel('Age')
plt.ylabel('$R^2$ of regression of $\log(OOP_{t})$ on $\log(OOP_{t-1})$')
plt.legend(['HRS data', 'Model'], loc=1)
plt.show()
# Calculate mortality probability by age and income quintile in simulated data
MortByIncAge_data = Data.MortByIncAge
MortByIncAge_sim = np.zeros((5, 15))
MortByAge_sim = np.zeros(15)
for t in range(15):
THESE = np.logical_and(Active[t, :], InDataSpan[t, :])
Weight = WeightHist[t + 1, THESE]
WeightSum = np.sum(Weight)
Mort = MortHist[t + 1, THESE]
MortByAge_sim[t] = np.dot(Mort, Weight) / WeightSum
for i in range(5):
right_inc = IncQuint == i + 1
these = np.logical_and(THESE, right_inc)
Mort = MortHist[t + 1, these]
Weight = WeightHist[t + 1, these]
WeightSum = np.sum(Weight)
MortByIncAge_sim[i, t] = np.dot(Mort, Weight) / WeightSum
# Plot mortality probability by age and income quintile
income_colors = ['b', 'r', 'g', 'm', 'c']
for i in range(5):
plt.plot(AgeVec, MortByIncAge_sim[i, :] - MortByAge_sim,
'-' + income_colors[i])
for i in range(5):
plt.plot(AgeVec, MortByIncAge_data[i, :] - MortByAge_sim,
'.' + income_colors[i])
plt.xlabel('Age')
plt.ylabel('Relative death probability (biannual)')
plt.title('Death Probability by Income Quintile')
plt.legend([
'Bottom quintile', 'Second quintile', 'Third quintile',
'Fourth quintile', 'Top quintile'
],
loc=2)
plt.savefig('../Figures/MortByIncAge.pdf')
plt.show()
# Plot the 99% confidence band of the health production function
mean = np.array([-2.13369276099, 1.71842956397])
covar = np.array([[0.02248322, 0.01628292], [0.01628308, 0.01564192]])
dstn = multivariate_normal(mean, covar)
N = 10000
M = 201
draws = dstn.rvs(10000)
MedVec = np.linspace(0., 1.5, M)
func_data = np.zeros((N, M))
def makeHealthProdFunc(LogSlope, LogCurve):
LogJerk = 15.6
tempw = np.exp(LogJerk)
HealthProd0 = 1. - tempw
tempx = np.exp(
LogSlope) # Slope of health production function at iLvl=0
HealthProd2 = np.exp(LogJerk - LogCurve)
HealthProdFunc = lambda i: tempx / HealthProd0 * (
(i * HealthProd2**(
(1. - HealthProd0) / HealthProd0) + HealthProd2**
(1. / HealthProd0))**HealthProd0 - HealthProd2)
return HealthProdFunc
for n in range(N):
f = makeHealthProdFunc(draws[n, 0], draws[n, 1])
func_data[n, :] = f(MedVec)
f = makeHealthProdFunc(Params.test_param_vec[25],
Params.test_param_vec[26])
CI_array = np.zeros((M, 2))
for m in range(M):
CI_array[m, :] = getPercentiles(func_data[:, m],
percentiles=[0.025, 0.975])
health_prod = f(MedVec)
plt.plot(MedVec, health_prod, '-r')
plt.plot(MedVec, CI_array[:, 0], '--k', linewidth=0.5)
plt.plot(MedVec, CI_array[:, 1], '--k', linewidth=0.5)
plt.xlim([-0.005, 1.5])
plt.ylim([0., None])
plt.xlabel('Health investment $n_{it}$, \$10,000 (y2000)')
plt.ylabel('Health produced ')
plt.title('Estimated Health Production Function')
plt.legend([
'Estimated health production function',
'Pointwise 95% confidence bounds'
],
loc=4)
plt.savefig('../Figures/HealthProdFunc.pdf')
plt.show()
BasicType.vFuncBool = False # just in case it was set to True above
my_agent_list = []
CRRA_list = np.linspace(
1, 8, type_count) # All the values that CRRA will take on
for i in range(type_count):
this_agent = deepcopy(BasicType) # Make a new copy of the basic type
this_agent.assignParameters(
CRRA=CRRA_list[i]) # Give it a unique CRRA value
my_agent_list.append(this_agent) # Addd it to the list of agent types
# Make a list of commands to be run in parallel; these should be methods of ConsumerType
do_this_stuff = ['updateSolutionTerminal()', 'solve()', 'unpack_cFunc()']
# Solve the model for each type by looping over the types (not multithreading)
start_time = clock()
multiThreadCommandsFake(my_agent_list,
do_this_stuff) # Fake multithreading, just loops
end_time = clock()
print('Solving ' + str(type_count) +
' types without multithreading took ' +
mystr(end_time - start_time) + ' seconds.')
# Plot the consumption functions for all types on one figure
plotFuncs([this_type.cFunc[0] for this_type in my_agent_list], 0, 5)
# Delete the solution for each type to make sure we're not just faking it
for i in range(type_count):
my_agent_list[i].solution = None
my_agent_list[i].cFunc = None
my_agent_list[i].time_vary.remove('solution')
my_agent_list[i].time_vary.remove('cFunc')
# Make many copies of the basic type, each with a different risk aversion
BasicType.vFuncBool = False # just in case it was set to True above
my_agent_list = []
CRRA_list = np.linspace(1,8,type_count) # All the values that CRRA will take on
for i in range(type_count):
this_agent = deepcopy(BasicType) # Make a new copy of the basic type
this_agent.assignParameters(CRRA = CRRA_list[i]) # Give it a unique CRRA value
my_agent_list.append(this_agent) # Addd it to the list of agent types
# Make a list of commands to be run in parallel; these should be methods of ConsumerType
do_this_stuff = ['updateSolutionTerminal()','solve()','unpackcFunc()']
# Solve the model for each type by looping over the types (not multithreading)
start_time = clock()
multiThreadCommandsFake(my_agent_list, do_this_stuff) # Fake multithreading, just loops
end_time = clock()
print('Solving ' + str(type_count) + ' types without multithreading took ' + mystr(end_time-start_time) + ' seconds.')
# Plot the consumption functions for all types on one figure
plotFuncs([this_type.cFunc[0] for this_type in my_agent_list],0,5)
# Delete the solution for each type to make sure we're not just faking it
for i in range(type_count):
my_agent_list[i].solution = None
my_agent_list[i].cFunc = None
my_agent_list[i].time_vary.remove('solution')
my_agent_list[i].time_vary.remove('cFunc')
# And here's HARK's initial attempt at multithreading:
start_time = clock()
plotFunc(BasicType.solution[0].vFunc,0.2,5)
# Make copies of the basic type, each with a different risk aversion
BasicType.calc_vFunc = False
my_agent_list = []
#rho_list = np.random.permutation(np.linspace(1,8,type_count))
rho_list = np.linspace(1,8,type_count)
for i in range(type_count):
this_agent = deepcopy(BasicType)
this_agent.assignParameters(rho = rho_list[i])
my_agent_list.append(this_agent)
do_this_stuff = ['updateSolutionTerminal()','solve()','unpack_cFunc()']
# Solve the model for each type by looping over the types (not multithreading)
start_time = time()
multiThreadCommandsFake(my_agent_list, do_this_stuff)
end_time = time()
print('Solving ' + str(type_count) + ' types without multithreading took ' + mystr(end_time-start_time) + ' seconds.')
# Plot the consumption functions for all types on one figure
plotFuncs([this_type.cFunc[0] for this_type in my_agent_list],0,5)
# Delete the solution for each type to make sure we're not just faking it
for i in range(type_count):
my_agent_list[i].solution = None
my_agent_list[i].cFunc = None
# And here's my shitty, shitty attempt at multithreading:
start_time = time()
multiThreadCommands(my_agent_list, do_this_stuff)
end_time = time()
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()
Target level of capital-to-output ratio.
Returns
-------
my_diff : float
Difference between simulated and target capital-to-output ratios.
'''
if type(DiscFac) in (list,np.ndarray,np.array):
DiscFac = DiscFac[0]
<<<<<<< HEAD
DiscFac_list = approxUniform(DiscFac,nabla,N)
=======
DiscFac_list = approxUniform(N,DiscFac-nabla,DiscFac+nabla)[1] # only take values, not probs
>>>>>>> eeb37f24755d0c683c9d9efbe5e7447425c98b86
assignBetaDistribution(type_list,DiscFac_list)
multiThreadCommandsFake(type_list,beta_point_commands)
my_diff = calculateKYratioDifference(np.vstack((this_type.W_history for this_type in type_list)),
np.tile(weights/float(N),N),total_output,target)
return my_diff
mystr = lambda number : "{:.3f}".format(number)
'''
Truncates a float at exactly three decimal places when displaying as a string.
'''
def makeCSTWresults(DiscFac,nabla,save_name=None):
'''
Produces a variety of results for the cstwMPC paper (usually after estimating).
Parameters
