incorporate initial distributions
@@ -27,7 +27,7 @@ from HARK.Calibration.Calibration import (
)
from HARK.datasets.life_tables.us_ssa.SSATools import parse_ssa_life_table
-
+from HARK.datasets.SCF.WealthIncomeDist.parser import income_wealth_dists_from_scf
import matplotlib.pyplot as plt
import pandas as pd
from copy import copy
@@ -35,9 +35,15 @@ from copy import copy
# %% Alter calibration
birth_age = 21
death_age = 90
+education = 'HS'
+# Income specification
income_params = ParseIncomeSpec(age_min = birth_age, age_max = death_age,
- **Cagetti_income['College'], SabelhausSong=True)
+ **CGM_income[education], SabelhausSong=True)
+
+# Initial distribution of wealth and permanent income
+dist_params = income_wealth_dists_from_scf(age = birth_age,
+ education = education)
# We need survival probabilities only up to death_age-1, because survival
# probability at death_age is 1.
@@ -49,7 +55,8 @@ time_params = ParseTimeParams(age_birth = birth_age, age_death = death_age)
params = copy(init_lifecycle)
params.update(time_params)
params.update(income_params)
-params.update({'LivPrb': liv_prb, 'aNrmInitMean': -100})
+params.update(dist_params)
+params.update({'LivPrb': liv_prb})
# %% Create and solve agent
Agent = IndShockConsumerType(**params)
@@ -60,7 +67,7 @@ Agent.solve()
# Setup
# Number of agents and periods in the simulation.
-Agent.AgentCount = 50
+Agent.AgentCount = 500
Agent.T_sim = 200
# Set up the variables we want to keep track of.
