def calcStats(self, aLvlNow, pLvlNow, MPCnow, TranShkNow, EmpNow, t_age,
LorenzBool, ManyStatsBool):
'''
Calculate various statistics about the current population in the economy.
Parameters
----------
aLvlNow : [np.array]
Arrays with end-of-period assets, listed by each ConsumerType in self.agents.
pLvlNow : [np.array]
Arrays with permanent income levels, listed by each ConsumerType in self.agents.
MPCnow : [np.array]
Arrays with marginal propensity to consume, listed by each ConsumerType in self.agents.
TranShkNow : [np.array]
Arrays with transitory income shocks, listed by each ConsumerType in self.agents.
EmpNow : [np.array]
Arrays with employment states: True if employed, False otherwise.
t_age : [np.array]
Arrays with periods elapsed since model entry, listed by each ConsumerType in self.agents.
LorenzBool: bool
Indicator for whether the Lorenz target points should be calculated. Usually False,
only True when DiscFac has been identified for a particular nabla.
ManyStatsBool: bool
Indicator for whether a lot of statistics for tables should be calculated. Usually False,
only True when parameters have been estimated and we want values for tables.
Returns
-------
None
'''
# Combine inputs into single arrays
aLvl = np.hstack(aLvlNow)
pLvl = np.hstack(pLvlNow)
age = np.hstack(t_age)
TranShk = np.hstack(TranShkNow)
Emp = np.hstack(EmpNow)
# Calculate the capital to income ratio in the economy
CohortWeight = self.PopGroFac**(-age)
CapAgg = np.sum(aLvl * CohortWeight)
IncAgg = np.sum(pLvl * TranShk * CohortWeight)
KtoYnow = CapAgg / IncAgg
self.KtoYnow = KtoYnow
# Store Lorenz data if requested
self.LorenzLong = np.nan
if LorenzBool:
order = np.argsort(aLvl)
aLvl = aLvl[order]
CohortWeight = CohortWeight[order]
wealth_shares = getLorenzShares(aLvl,
weights=CohortWeight,
percentiles=self.LorenzPercentiles,
presorted=True)
self.Lorenz = wealth_shares
if ManyStatsBool:
self.LorenzLong = getLorenzShares(aLvl,
weights=CohortWeight,
percentiles=np.arange(
0.01, 1.0, 0.01),
presorted=True)
else:
self.Lorenz = np.nan # Store nothing if we don't want Lorenz data
# Calculate a whole bunch of statistics if requested
if ManyStatsBool:
# Reshape other inputs
MPC = np.hstack(MPCnow)
# Sort other data items if aLvl and CohortWeight were sorted
if LorenzBool:
pLvl = pLvl[order]
MPC = MPC[order]
TranShk = TranShk[order]
age = age[order]
Emp = Emp[order]
aNrm = aLvl / pLvl # Normalized assets (wealth ratio)
IncLvl = TranShk * pLvl # Labor income this period
# Calculate overall population MPC and by subpopulations
#MPCsixmonths = 1.0 - 0.25*((1.0 - MPC) + (1.0 - MPC)**2 + (1.0 - MPC)**3 + (1.0 - MPC)**4)
MPCsixmonths = 1.0 - (1.0 - MPC)**2
self.MPCall = np.sum(
MPCsixmonths * CohortWeight) / np.sum(CohortWeight)
employed = Emp
unemployed = np.logical_not(employed)
if self.T_retire > 0: # Adjust for the lifecycle model, where agents might be retired instead
unemployed = np.logical_and(unemployed, age < self.T_retire)
employed = np.logical_and(employed, age < self.T_retire)
retired = age >= self.T_retire
else:
retired = np.zeros_like(unemployed, dtype=bool)
self.MPCunemployed = np.sum(
MPCsixmonths[unemployed] * CohortWeight[unemployed]) / np.sum(
CohortWeight[unemployed])
self.MPCemployed = np.sum(
MPCsixmonths[employed] * CohortWeight[employed]) / np.sum(
CohortWeight[employed])
self.MPCretired = np.sum(
MPCsixmonths[retired] * CohortWeight[retired]) / np.sum(
CohortWeight[retired])
self.MPCbyWealthRatio = calcSubpopAvg(MPCsixmonths, aNrm,
self.cutoffs, CohortWeight)
self.MPCbyIncome = calcSubpopAvg(MPCsixmonths, IncLvl,
self.cutoffs, CohortWeight)
# Calculate the wealth quintile distribution of "hand to mouth" consumers
quintile_cuts = getPercentiles(aLvl,
weights=CohortWeight,
percentiles=[0.2, 0.4, 0.6, 0.8])
wealth_quintiles = np.ones(aLvl.size, dtype=int)
wealth_quintiles[aLvl > quintile_cuts[0]] = 2
wealth_quintiles[aLvl > quintile_cuts[1]] = 3
wealth_quintiles[aLvl > quintile_cuts[2]] = 4
wealth_quintiles[aLvl > quintile_cuts[3]] = 5
MPC_cutoff = getPercentiles(
MPCsixmonths, weights=CohortWeight, percentiles=[
2.0 / 3.0
]) # Looking at consumers with MPCs in the top 1/3
these = MPCsixmonths > MPC_cutoff
in_top_third_MPC = wealth_quintiles[these]
temp_weights = CohortWeight[these]
hand_to_mouth_total = np.sum(temp_weights)
hand_to_mouth_pct = []
for q in range(1, 6):
hand_to_mouth_pct.append(
np.sum(temp_weights[in_top_third_MPC == q]) /
hand_to_mouth_total)
self.HandToMouthPct = np.array(hand_to_mouth_pct)
else: # If we don't want these stats, just put empty values in history
self.MPCall = np.nan
self.MPCunemployed = np.nan
self.MPCemployed = np.nan
self.MPCretired = np.nan
self.MPCbyWealthRatio = np.nan
self.MPCbyIncome = np.nan
self.HandToMouthPct = np.nan
def makeCSTWstats(DiscFac,nabla,this_type_list,age_weight,lorenz_distance=0.0,save_name=None):
'''
Displays (and saves) a bunch of statistics. Separate from makeCSTWresults()
for compatibility with the aggregate shock model.
Parameters
----------
DiscFac : float
Center of the uniform distribution of discount factors
nabla : float
Width of the uniform distribution of discount factors
this_type_list : [cstwMPCagent]
List of agent types in the economy.
age_weight : np.array
Age-conditional array of weights for the wealth data.
lorenz_distance : float
Distance between simulated and actual Lorenz curves, for display.
save_name : string
Name to save the calculated results, for later use in producing figures
and tables, etc.
Returns
-------
none
'''
sim_length = this_type_list[0].sim_periods
sim_wealth = (np.vstack((this_type.W_history for this_type in this_type_list))).flatten()
sim_wealth_short = (np.vstack((this_type.W_history[0:sim_length,:] for this_type in this_type_list))).flatten()
sim_kappa = (np.vstack((this_type.kappa_history for this_type in this_type_list))).flatten()
sim_income = (np.vstack((this_type.pHist[0:sim_length,:]*np.asarray(this_type.TranShkHist[0:sim_length,:]) for this_type in this_type_list))).flatten()
sim_ratio = (np.vstack((this_type.W_history[0:sim_length,:]/this_type.pHist[0:sim_length,:] for this_type in this_type_list))).flatten()
if Params.do_lifecycle:
sim_unemp = (np.vstack((np.vstack((this_type.IncUnemp == this_type.TranShkHist[0:Params.working_T,:],np.zeros((Params.retired_T+1,this_type_list[0].Nagents),dtype=bool))) for this_type in this_type_list))).flatten()
sim_emp = (np.vstack((np.vstack((this_type.IncUnemp != this_type.TranShkHist[0:Params.working_T,:],np.zeros((Params.retired_T+1,this_type_list[0].Nagents),dtype=bool))) for this_type in this_type_list))).flatten()
sim_ret = (np.vstack((np.vstack((np.zeros((Params.working_T,this_type_list[0].Nagents),dtype=bool),np.ones((Params.retired_T+1,this_type_list[0].Nagents),dtype=bool))) for this_type in this_type_list))).flatten()
else:
sim_unemp = np.vstack((this_type.IncUnemp == this_type.TranShkHist[0:sim_length,:] for this_type in this_type_list)).flatten()
sim_emp = np.vstack((this_type.IncUnemp != this_type.TranShkHist[0:sim_length,:] for this_type in this_type_list)).flatten()
sim_ret = np.zeros(sim_emp.size,dtype=bool)
sim_weight_all = np.tile(np.repeat(age_weight,this_type_list[0].Nagents),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 this_type_list)),axis=1)).reshape((Params.pref_type_count*3,DropoutType.T_total+1))
kappa_mean_by_age_pref = np.zeros((Params.pref_type_count,DropoutType.T_total+1)) + 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_all)
kappa_all = calcWeightedAvg(np.vstack((this_type.kappa_history for this_type in this_type_list)),np.tile(age_weight/float(Params.pref_type_count),Params.pref_type_count))
kappa_unemp = np.sum(sim_kappa[sim_unemp]*sim_weight_all[sim_unemp])/np.sum(sim_weight_all[sim_unemp])
kappa_emp = np.sum(sim_kappa[sim_emp]*sim_weight_all[sim_emp])/np.sum(sim_weight_all[sim_emp])
kappa_ret = np.sum(sim_kappa[sim_ret]*sim_weight_all[sim_ret])/np.sum(sim_weight_all[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 = calcSubpopAvg(sim_kappa,sim_ratio,my_cutoffs,sim_weight_all)
kappa_by_income_groups = calcSubpopAvg(sim_kappa,sim_income,my_cutoffs,sim_weight_all)
quintile_points = getPercentiles(sim_wealth_short,weights=sim_weight_all,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 = getPercentiles(sim_kappa,weights=sim_weight_all,percentiles=[2.0/3.0])
these_quintiles = wealth_quintiles[sim_kappa > MPC_cutoff]
these_weights = sim_weight_all[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 DiscFac=' + str(DiscFac) + ', 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()
def makeCSTWstats(DiscFac,
nabla,
this_type_list,
age_weight,
lorenz_distance=0.0,
save_name=None):
'''
Displays (and saves) a bunch of statistics. Separate from makeCSTWresults()
for compatibility with the aggregate shock model.
Parameters
----------
DiscFac : float
Center of the uniform distribution of discount factors
nabla : float
Width of the uniform distribution of discount factors
this_type_list : [cstwMPCagent]
List of agent types in the economy.
age_weight : np.array
Age-conditional array of weights for the wealth data.
lorenz_distance : float
Distance between simulated and actual Lorenz curves, for display.
save_name : string
Name to save the calculated results, for later use in producing figures
and tables, etc.
Returns
-------
none
'''
sim_length = this_type_list[0].sim_periods
sim_wealth = (np.vstack(
(this_type.W_history for this_type in this_type_list))).flatten()
sim_wealth_short = (np.vstack(
(this_type.W_history[0:sim_length, :]
for this_type in this_type_list))).flatten()
sim_kappa = (np.vstack(
(this_type.kappa_history for this_type in this_type_list))).flatten()
sim_income = (np.vstack((this_type.pHist[0:sim_length, :] *
np.asarray(this_type.TranShkHist[0:sim_length, :])
for this_type in this_type_list))).flatten()
sim_ratio = (np.vstack((this_type.W_history[0:sim_length, :] /
this_type.pHist[0:sim_length, :]
for this_type in this_type_list))).flatten()
if Params.do_lifecycle:
sim_unemp = (np.vstack((np.vstack((
this_type.IncUnemp == this_type.TranShkHist[0:Params.working_T, :],
np.zeros((Params.retired_T + 1, this_type_list[0].Nagents),
dtype=bool)))
for this_type in this_type_list))).flatten()
sim_emp = (np.vstack((np.vstack(
(this_type.IncUnemp !=
this_type.TranShkHist[0:Params.working_T, :],
np.zeros((Params.retired_T + 1, this_type_list[0].Nagents),
dtype=bool)))
for this_type in this_type_list))).flatten()
sim_ret = (np.vstack((np.vstack(
(np.zeros((Params.working_T, this_type_list[0].Nagents),
dtype=bool),
np.ones((Params.retired_T + 1, this_type_list[0].Nagents),
dtype=bool)))
for this_type in this_type_list))).flatten()
else:
sim_unemp = np.vstack(
(this_type.IncUnemp == this_type.TranShkHist[0:sim_length, :]
for this_type in this_type_list)).flatten()
sim_emp = np.vstack(
(this_type.IncUnemp != this_type.TranShkHist[0:sim_length, :]
for this_type in this_type_list)).flatten()
sim_ret = np.zeros(sim_emp.size, dtype=bool)
sim_weight_all = np.tile(np.repeat(age_weight, this_type_list[0].Nagents),
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 this_type_list)),
axis=1)).reshape(
(Params.pref_type_count * 3,
DropoutType.T_total + 1))
kappa_mean_by_age_pref = np.zeros(
(Params.pref_type_count, DropoutType.T_total + 1)) + 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_all)
kappa_all = calcWeightedAvg(
np.vstack((this_type.kappa_history for this_type in this_type_list)),
np.tile(age_weight / float(Params.pref_type_count),
Params.pref_type_count))
kappa_unemp = np.sum(
sim_kappa[sim_unemp] * sim_weight_all[sim_unemp]) / np.sum(
sim_weight_all[sim_unemp])
kappa_emp = np.sum(sim_kappa[sim_emp] * sim_weight_all[sim_emp]) / np.sum(
sim_weight_all[sim_emp])
kappa_ret = np.sum(sim_kappa[sim_ret] * sim_weight_all[sim_ret]) / np.sum(
sim_weight_all[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 = calcSubpopAvg(sim_kappa, sim_ratio, my_cutoffs,
sim_weight_all)
kappa_by_income_groups = calcSubpopAvg(sim_kappa, sim_income, my_cutoffs,
sim_weight_all)
quintile_points = getPercentiles(sim_wealth_short,
weights=sim_weight_all,
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 = getPercentiles(sim_kappa,
weights=sim_weight_all,
percentiles=[2.0 / 3.0])
these_quintiles = wealth_quintiles[sim_kappa > MPC_cutoff]
these_weights = sim_weight_all[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 DiscFac=' + str(DiscFac) + ', 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()
def calcStats(self,aLvlNow,pLvlNow,MPCnow,TranShkNow,EmpNow,t_age,LorenzBool,ManyStatsBool):
'''
Calculate various statistics about the current population in the economy.
Parameters
----------
aLvlNow : [np.array]
Arrays with end-of-period assets, listed by each ConsumerType in self.agents.
pLvlNow : [np.array]
Arrays with permanent income levels, listed by each ConsumerType in self.agents.
MPCnow : [np.array]
Arrays with marginal propensity to consume, listed by each ConsumerType in self.agents.
TranShkNow : [np.array]
Arrays with transitory income shocks, listed by each ConsumerType in self.agents.
EmpNow : [np.array]
Arrays with employment states: True if employed, False otherwise.
t_age : [np.array]
Arrays with periods elapsed since model entry, listed by each ConsumerType in self.agents.
LorenzBool: bool
Indicator for whether the Lorenz target points should be calculated. Usually False,
only True when DiscFac has been identified for a particular nabla.
ManyStatsBool: bool
Indicator for whether a lot of statistics for tables should be calculated. Usually False,
only True when parameters have been estimated and we want values for tables.
Returns
-------
None
'''
# Combine inputs into single arrays
aLvl = np.hstack(aLvlNow)
pLvl = np.hstack(pLvlNow)
age = np.hstack(t_age)
TranShk = np.hstack(TranShkNow)
Emp = np.hstack(EmpNow)
# Calculate the capital to income ratio in the economy
CohortWeight = self.PopGroFac**(-age)
CapAgg = np.sum(aLvl*CohortWeight)
IncAgg = np.sum(pLvl*TranShk*CohortWeight)
KtoYnow = CapAgg/IncAgg
self.KtoYnow = KtoYnow
# Store Lorenz data if requested
self.LorenzLong = np.nan
if LorenzBool:
order = np.argsort(aLvl)
aLvl = aLvl[order]
CohortWeight = CohortWeight[order]
wealth_shares = getLorenzShares(aLvl,weights=CohortWeight,percentiles=self.LorenzPercentiles,presorted=True)
self.Lorenz = wealth_shares
if ManyStatsBool:
self.LorenzLong = getLorenzShares(aLvl,weights=CohortWeight,percentiles=np.arange(0.01,1.0,0.01),presorted=True)
else:
self.Lorenz = np.nan # Store nothing if we don't want Lorenz data
# Calculate a whole bunch of statistics if requested
if ManyStatsBool:
# Reshape other inputs
MPC = np.hstack(MPCnow)
# Sort other data items if aLvl and CohortWeight were sorted
if LorenzBool:
pLvl = pLvl[order]
MPC = MPC[order]
TranShk = TranShk[order]
age = age[order]
Emp = Emp[order]
aNrm = aLvl/pLvl # Normalized assets (wealth ratio)
IncLvl = TranShk*pLvl # Labor income this period
# Calculate overall population MPC and by subpopulations
MPCannual = 1.0 - (1.0 - MPC)**4
self.MPCall = np.sum(MPCannual*CohortWeight)/np.sum(CohortWeight)
employed = Emp
unemployed = np.logical_not(employed)
if self.T_retire > 0: # Adjust for the lifecycle model, where agents might be retired instead
unemployed = np.logical_and(unemployed,age < self.T_retire)
employed = np.logical_and(employed,age < self.T_retire)
retired = age >= self.T_retire
else:
retired = np.zeros_like(unemployed,dtype=bool)
self.MPCunemployed = np.sum(MPCannual[unemployed]*CohortWeight[unemployed])/np.sum(CohortWeight[unemployed])
self.MPCemployed = np.sum(MPCannual[employed]*CohortWeight[employed])/np.sum(CohortWeight[employed])
self.MPCretired = np.sum(MPCannual[retired]*CohortWeight[retired])/np.sum(CohortWeight[retired])
self.MPCbyWealthRatio = calcSubpopAvg(MPCannual,aNrm,self.cutoffs,CohortWeight)
self.MPCbyIncome = calcSubpopAvg(MPCannual,IncLvl,self.cutoffs,CohortWeight)
# Calculate the wealth quintile distribution of "hand to mouth" consumers
quintile_cuts = getPercentiles(aLvl,weights=CohortWeight,percentiles=[0.2, 0.4, 0.6, 0.8])
wealth_quintiles = np.ones(aLvl.size,dtype=int)
wealth_quintiles[aLvl > quintile_cuts[0]] = 2
wealth_quintiles[aLvl > quintile_cuts[1]] = 3
wealth_quintiles[aLvl > quintile_cuts[2]] = 4
wealth_quintiles[aLvl > quintile_cuts[3]] = 5
MPC_cutoff = getPercentiles(MPCannual,weights=CohortWeight,percentiles=[2.0/3.0]) # Looking at consumers with MPCs in the top 1/3
these = MPCannual > MPC_cutoff
in_top_third_MPC = wealth_quintiles[these]
temp_weights = CohortWeight[these]
hand_to_mouth_total = np.sum(temp_weights)
hand_to_mouth_pct = []
for q in range(1,6):
hand_to_mouth_pct.append(np.sum(temp_weights[in_top_third_MPC == q])/hand_to_mouth_total)
self.HandToMouthPct = np.array(hand_to_mouth_pct)
else: # If we don't want these stats, just put empty values in history
self.MPCall = np.nan
self.MPCunemployed = np.nan
self.MPCemployed = np.nan
self.MPCretired = np.nan
self.MPCbyWealthRatio = np.nan
self.MPCbyIncome = np.nan
self.HandToMouthPct = np.nan
