generalize params

code/estimark/agents.py

@@ -9,36 +9,15 @@
 income as defined in ConsIndShockModel.
 """
 
-# Parameters for the consumer type and the estimation
-
 from HARK.ConsumptionSaving.ConsBequestModel import (
     BequestWarmGlowConsumerType,
     BequestWarmGlowPortfolioType,
 )
-
-# Import modules from core HARK libraries:
-# The consumption-saving micro model
 from HARK.ConsumptionSaving.ConsIndShockModel import IndShockConsumerType
 from HARK.ConsumptionSaving.ConsPortfolioModel import PortfolioConsumerType
 from HARK.ConsumptionSaving.ConsWealthPortfolioModel import WealthPortfolioConsumerType
 from HARK.core import AgentType
 
-# Method for sampling from a discrete distribution
-
-# Estimation methods
-
-# Set booleans to determine which tasks should be done
-# Which agent type to estimate ("IndShock" or "Portfolio")
-local_estimation_agent = "IndShock"
-local_estimate_model = True  # Whether to estimate the model
-# Whether to get standard errors via bootstrap
-local_compute_standard_errors = False
-# Whether to compute a measure of estimates' sensitivity to moments
-local_compute_sensitivity = True
-# Whether to make a contour map of the objective function
-local_make_contour_plot = True
-
-
 # =====================================================
 # Define objects and functions used for the estimation
 # =====================================================

code/estimark/estimation.py

@@ -136,9 +136,11 @@ def weighted_median(values, weights):
     return median
 
 
-def get_targeted_moments(data=scf_data, weights=scf_weights, groups=scf_groups):
+def get_targeted_moments(
+    data=scf_data, weights=scf_weights, groups=scf_groups, mapping=scf_mapping
+):
     # Initialize
-    group_count = len(scf_mapping)
+    group_count = len(mapping)
     target_moments = np.zeros(group_count)
 
     for g in range(group_count):
@@ -164,13 +166,15 @@ def get_initial_guess(agent_name):
 
 # Define the objective function for the simulated method of moments estimation
 # todo: params, bounds, agent
-def simulate_moments(DiscFacAdj, CRRA, agent):
+def simulate_moments(params, agent):
     """
     A quick check to make sure that the parameter values are within bounds.
     Far flung falues of DiscFacAdj or CRRA might cause an error during solution or
     simulation, so the objective function doesn't even bother with them.
     """
 
+    DiscFacAdj, CRRA = params
+
     bounds_DiscFacAdj = options["bounds_DiscFacAdj"]
     bounds_CRRA = options["bounds_CRRA"]
 
@@ -210,7 +214,7 @@ def simulate_moments(DiscFacAdj, CRRA, agent):
     return sim_moments
 
 
-def smm_obj_func(DiscFacAdj, CRRA, agent, moments):
+def smm_obj_func(params, agent, moments):
     """
     The objective function for the SMM estimation.  Given values of discount factor
     adjuster DiscFacAdj, coeffecient of relative risk aversion CRRA, a base consumer
@@ -258,7 +262,7 @@ def smm_obj_func(DiscFacAdj, CRRA, agent, moments):
         median wealth-to-permanent-income ratio in the simulation.
     """
 
-    sim_moments = simulate_moments(DiscFacAdj, CRRA, agent)
+    sim_moments = simulate_moments(params, agent)
     errors = moments - sim_moments
     loss = np.dot(errors, errors)
 
@@ -313,8 +317,7 @@ def calculate_se_bootstrap(initial_estimate, N, agent, seed=0, verbose=False):
         # Make a temporary function for use in this estimation run
         def smm_obj_func_bootstrap(params):
             return smm_obj_func(
-                DiscFacAdj=params[0],
-                CRRA=params[1],
+                params,
                 agent=agent,
                 moments=bootstrap_moments,
             )
@@ -358,8 +361,7 @@ def smm_obj_func_redux(params):
         list representing [DiscFacAdj,CRRA].
         """
         return smm_obj_func(
-            DiscFacAdj=params[0],
-            CRRA=params[1],
+            params,
             agent=estimation_agent,
             moments=target_moments,
         )
@@ -445,9 +447,8 @@ def do_compute_sensitivity(agent_name, estimation_agent, model_estimate, initial
 
     # Find the Jacobian of the function that simulates moments
     def simulate_moments_redux(params):
-        moments = simulate_moments(params[0], params[1], agent=estimation_agent)
 
-        return moments
+        return simulate_moments(params, agent=estimation_agent)
 
     n_moments = len(scf_mapping)
     jac = np.array(
@@ -507,8 +508,7 @@ def do_make_contour_plot(agent_name, estimation_agent, model_estimate, target_mo
         for k in range(grid_density):
             CRRA = CRRA_list[k]
             smm_obj_levels[j, k] = smm_obj_func(
-                DiscFacAdj,
-                CRRA,
+                np.array([DiscFacAdj, CRRA]),
                 agent=estimation_agent,
                 moments=target_moments,
             )

code/estimark/parameters.py

@@ -3,8 +3,6 @@
 model.  The empirical data is stored in a separate csv file and is loaded in setup_scf_data.
 """
 
-from pathlib import Path
-
 import numpy as np
 from HARK.Calibration.Income.IncomeTools import CGM_income, parse_income_spec
 from HARK.ConsumptionSaving.ConsIndShockModel import init_lifecycle
@@ -64,7 +62,7 @@
 
 # Age-varying discount factors over the lifecycle, lifted from Cagetti (2003)
 # Get the directory containing the current file and construct the full path to the CSV file
-csv_file_path = Path(__file__).resolve().parent / ".." / "data" / "Cagetti2003.csv"
+csv_file_path = "code/data/Cagetti2003.csv"
 timevary_DiscFac = np.genfromtxt(csv_file_path) * 0.0 + 1.0  # todo
 constant_DiscFac = np.ones_like(timevary_DiscFac)
 

code/estimark/scf.py

@@ -2,14 +2,12 @@
 Sets up the SCF data for use in the EstimatingMicroDSOPs estimation.
 """
 
-from pathlib import Path
-
 import numpy as np  # Numerical Python
 import pandas as pd
 from estimark.parameters import initial_age
 
 # Get the directory containing the current file and construct the full path to the CSV file
-csv_file_path = Path(__file__).resolve().parent / ".." / "data" / "SCFdata.csv"
+csv_file_path = "code/data/SCFdata.csv"
 
 # Read the CSV file
 scf_full_data = pd.read_csv(csv_file_path)