# Now change some of the parameters for the individual's problem to those of cjSOE
Params.init_cjSOE['CRRA'] = 2
Params.init_cjSOE['Rfree'] = 1.04**0.25
Params.init_cjSOE['PermGroFac'] = [
1.01**0.25
] # Indiviual-specific income growth (from experience, e.g.)
Params.init_cjSOE[
'PermGroFacAgg'] = 1.04**0.25 # Aggregate productivity growth
Params.init_cjSOE['LivPrb'] = [0.95**0.25] # Matches a short working life
PopGroFac_cjSOE = [
1.01**0.25
] # Irrelevant to the individual's choice; attach later to "market" economy object
# Instantiate the baseline agent type with the parameters defined above
BaselineType = cstwMPC.cstwMPCagent(**Params.init_cjSOE)
BaselineType.AgeDstn = np.array(1.0) # Fix the age distribution of agents
# Make desired number of agent types (to capture ex-ante heterogeneity)
EstimationAgentList = []
for n in range(Params.pref_type_count):
EstimationAgentList.append(deepcopy(BaselineType))
EstimationAgentList[n].seed = n # Give every instance a different seed
# %% {"code_folding": [0]}
# Make an economy for the consumers to live in
EstimationEconomy = cstwMPC.cstwMPCmarket(**Params.init_market)
EstimationEconomy.print_parallel_error_once = True # Avoids a bug in the code
EstimationEconomy.agents = EstimationAgentList
# Set total number of simulated agents in the population
if do_param_dist:
if do_agg_shocks:
Population = Params.pop_sim_agg_dist
else:
Population = Params.pop_sim_ind_dist
else:
if do_agg_shocks:
Population = Params.pop_sim_agg_point
else:
Population = Params.pop_sim_ind_point
# Make AgentTypes for estimation
if do_lifecycle:
DropoutType = cstwMPCagent(**Params.init_dropout)
DropoutType.AgeDstn = calcStationaryAgeDstn(DropoutType.LivPrb, True)
HighschoolType = deepcopy(DropoutType)
HighschoolType(**Params.adj_highschool)
HighschoolType.AgeDstn = calcStationaryAgeDstn(HighschoolType.LivPrb,
True)
CollegeType = deepcopy(DropoutType)
CollegeType(**Params.adj_college)
CollegeType.AgeDstn = calcStationaryAgeDstn(CollegeType.LivPrb, True)
DropoutType.update()
HighschoolType.update()
CollegeType.update()
EstimationAgentList = []
for n in range(pref_type_count):
EstimationAgentList.append(deepcopy(DropoutType))
EstimationAgentList.append(deepcopy(HighschoolType))