cohort_scale_array = np.tile(np.reshape(cohort_scale,(Params.total_T+1,1)),(1,Params.sim_pop_size))
# Make base consumer types for each education level
DropoutType = cstwMPCagent(**Params.init_dropout)
DropoutType.a_init = a_init
DropoutType.cohort_scale = cohort_scale_array
HighschoolType = deepcopy(DropoutType)
HighschoolType(**Params.adj_highschool)
CollegeType = deepcopy(DropoutType)
CollegeType(**Params.adj_college)
DropoutType.update()
HighschoolType.update()
CollegeType.update()
# Make initial distributions of permanent income for each education level
p_init_base = drawMeanOneLognormal(N=Params.sim_pop_size, sigma=Params.P0_sigma, seed=Params.P0_seed)
DropoutType.p_init = Params.P0_d*p_init_base
HighschoolType.p_init = Params.P0_h*p_init_base
CollegeType.p_init = Params.P0_c*p_init_base
# Set the type list for the lifecycle estimation
short_type_list = [DropoutType, HighschoolType, CollegeType]
spec_add = 'LC'
else:
# Make the base infinite horizon type and assign income shocks
InfiniteType = cstwMPCagent(**Params.init_infinite)
InfiniteType.tolerance = 0.0001
InfiniteType.a_init = 0*np.ones_like(a_init)
# Make histories of permanent income levels for the infinite horizon type
# Make base consumer types for each education level
DropoutType = cstwMPCagent(**Params.init_dropout)
DropoutType.a_init = a_init
DropoutType.cohort_scale = cohort_scale_array
HighschoolType = deepcopy(DropoutType)
HighschoolType(**Params.adj_highschool)
CollegeType = deepcopy(DropoutType)
CollegeType(**Params.adj_college)
DropoutType.update()
HighschoolType.update()
CollegeType.update()
# Make initial distributions of permanent income for each education level
p_init_base = drawMeanOneLognormal(N=Params.sim_pop_size,
sigma=Params.P0_sigma,
seed=Params.P0_seed)
DropoutType.p_init = Params.P0_d * p_init_base
HighschoolType.p_init = Params.P0_h * p_init_base
CollegeType.p_init = Params.P0_c * p_init_base
# Set the type list for the lifecycle estimation
short_type_list = [DropoutType, HighschoolType, CollegeType]
spec_add = 'LC'
else:
# Make the base infinite horizon type and assign income shocks
InfiniteType = cstwMPCagent(**Params.init_infinite)
InfiniteType.tolerance = 0.0001
InfiniteType.a_init = 0 * np.ones_like(a_init)
# contained in the HARK folder. Also import ConsumptionSavingModel
import sys
import os
sys.path.insert(0, os.path.abspath('../'))
sys.path.insert(0, os.path.abspath('../ConsumptionSaving'))
import numpy as np
import matplotlib.pyplot as plt
from HARKsimulation import drawMeanOneLognormal
N = 10000
T = 10000
PermShkStd = np.sqrt(0.00436)
LivPrb = 0.995
DiePrb = 1 - LivPrb
pLvl_t = np.ones(N)
pLvl_hist = np.ones((T,N))
for t in range(T):
draws = np.random.rand(N)
who_dies = draws < DiePrb
pLvl_t[who_dies] = 1.0
Shks = drawMeanOneLognormal(N,sigma=PermShkStd,seed=np.random.randint(0,2**31-1))
pLvl_t = pLvl_t*Shks
pLvl_hist[t,:] = pLvl_t
plt.plot(np.std(pLvl_hist,axis=1))
plt.show()
plt.plot(np.std(np.log(pLvl_hist),axis=1))
plt.show()