# ====== Make the list of consumer types for estimation ===========
#==================================================================
# Set target Lorenz points and K/Y ratio (MOVE THIS TO SetupParams)
if Params.do_liquid:
lorenz_target = np.array([0.0, 0.004, 0.025,0.117])
KY_target = 6.60
else: # This is hacky until I can find the liquid wealth data and import it
lorenz_target = getLorenzPercentiles(Params.SCF_wealth,weights=Params.SCF_weights,percentiles=Params.percentiles_to_match)
#lorenz_target = np.array([-0.002, 0.01, 0.053,0.171])
KY_target = 10.26
# Make a vector of initial wealth-to-permanent income ratios
w0_vector = generateDiscreteDraws(P=Params.w0_probs,
X=Params.w0_values,
N=Params.sim_pop_size,
seed=Params.w0_seed)
# Make the list of types for this run, whether infinite or lifecycle
if Params.do_lifecycle:
# Make base consumer types for each education level
DropoutType = cstwMPCagent(**Params.init_dropout)
DropoutType.w0 = w0_vector
HighschoolType = deepcopy(DropoutType)
HighschoolType(**Params.adj_highschool)
CollegeType = deepcopy(DropoutType)
CollegeType(**Params.adj_college)
DropoutType.update()
HighschoolType.update()
CollegeType.update()
# Set booleans to determine which tasks should be done
estimate_model = True
compute_standard_errors = False
make_contour_plot = False
#=====================================================
# Define objects and functions used for the estimation
#=====================================================
# Make a lifecycle consumer to be used for estimation, including simulated shocks
EstimationAgent = Model.ConsumerType(**Params.init_consumer_objects)
# Make histories of permanent and transitory shocks, plus an initial distribution of wealth
scriptR_shocks, xi_shocks = Model.generateIncomeShockHistoryLognormalUnemployment(EstimationAgent)
w0_vector = generateDiscreteDraws(P=Params.initial_wealth_income_ratio_probs,
X=Params.initial_wealth_income_ratio_vals,
N=Params.num_agents,
seed=Params.seed)
EstimationAgent.addIncomeShockPaths(scriptR_shocks,xi_shocks)
# Define the objective function for the estimation
def smmObjectiveFxn(beth, rho,
agent = EstimationAgent,
beth_bound = Params.beth_bound,
rho_bound = Params.rho_bound,
empirical_data = Data.w_to_y_data,
empirical_weights = Data.empirical_weights,
empirical_groups = Data.empirical_groups,
initial_wealth = w0_vector,
map_simulated_to_empirical_cohorts = Data.simulation_map_cohorts_to_age_indices):
'''
The objective function for the SMM estimation. Given values of discount-factor
