create do_max.py

econ-ark · Apr 12, 2023 · a06c0d3 · a06c0d3
1 parent 8c8ef20
commit a06c0d3
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Showing 12 changed files with 99 additions and 59 deletions.
diff --git a/Code/Options/__init__.py b/Code/Options/__init__.py
diff --git a/Code/Options/all_options.py b/Code/Options/all_options.py
@@ -67,3 +67,17 @@
     "do_agg_shocks": True,  # Solve the FBS aggregate shocks version of the model
     "do_liquid": False,  # Matches liquid assets data when True, net worth data when False
 }
+
+all_options["LCSpecPoint"] = {
+    "do_param_dist": False,  # Do param-dist version if True, param-point if False
+    "do_lifecycle": True,  # Use lifecycle model if True, perpetual youth if False
+    "do_agg_shocks": True,  # Solve the FBS aggregate shocks version of the model
+    "do_liquid": False,  # Matches liquid assets data when True, net worth data when False
+}
+
+all_options["LCSpecDist"] = {
+    "do_param_dist": True,  # Do param-dist version if True, param-point if False
+    "do_lifecycle": True,  # Use lifecycle model if True, perpetual youth if False
+    "do_agg_shocks": True,  # Solve the FBS aggregate shocks version of the model
+    "do_liquid": False,  # Matches liquid assets data when True, net worth data when False
+}
diff --git a/Code/Tests/test_results.py b/Code/Tests/test_results.py
@@ -1,8 +1,8 @@
 """
 To be run after `do_min.py`.
-Depends on having files in Results.
+Depends on having files in results.
 
-Will test the output files in Results for similarity with the results in the original
+Will test the output files in results for similarity with the results in the original
 cstwMPC paper.
 """
 import pathlib
@@ -128,7 +128,7 @@
 
 for filename, targets in results_files_and_targets.items():
     with open(
-        pathlib.PurePath(pathlib.Path(__file__).parent, "../Results", filename),
+        pathlib.PurePath(pathlib.Path(__file__).parent, "../results", filename),
         encoding="utf-8",
     ) as f:
         data = f.read()

diff --git a/Code/agents.py b/Code/agents.py
@@ -526,7 +526,7 @@ def show_many_stats(self, spec_name=None):
         # Save results to disk
         if spec_name is not None:
             with open(
-                self.my_file_path + "/Results/" + spec_name + "Results.txt",
+                self.my_file_path + "/results/" + spec_name + "Results.txt",
                 "w",
                 encoding="utf-8",
             ) as f:

diff --git a/Code/estimation.py b/Code/estimation.py
@@ -42,11 +42,7 @@
 from HARK.utilities import get_lorenz_shares
 from scipy.optimize import minimize, minimize_scalar, root_scalar
 
-from Code.agents import AggDoWAgent, AggDoWMarket, DoWAgent, DoWMarket
-
-
-def mystr(number):
-    return f"{number:.3f}"
+from code.agents import AggDoWAgent, AggDoWMarket, DoWAgent, DoWMarket
 
 
 def get_ky_ratio_difference(
@@ -145,7 +141,7 @@ def find_lorenz_distance_at_target_ky(
 
 
 def get_target_ky_and_find_lorenz_distance(
-    x, economy=None, param_name=None, param_count=None, dist_type=None
+    x, economy, param_name, param_count, dist_type
 ):
     center, spread = x
     # Make sure we actually calculate simulated Lorenz points
@@ -192,7 +188,7 @@ def calc_stationary_age_dstn(LivPrb, terminal_period):
     for t in range(top):
         MrkvArray[t, 0] = 1.0 - LivPrb[t]
         MrkvArray[t, t + 1] = LivPrb[t]
-    MrkvArray[t + 1, 0] = 1.0
+        MrkvArray[t + 1, 0] = 1.0
 
     w, v = np.linalg.eig(np.transpose(MrkvArray))
     idx = (np.abs(w - 1.0)).argmin()
@@ -321,10 +317,10 @@ def make_agents(options, params, agent_class, param_count):
         dropout_type = agent_class(**params.init_dropout)
         dropout_type.AgeDstn = calc_stationary_age_dstn(dropout_type.LivPrb, True)
         highschool_type = deepcopy(dropout_type)
-        highschool_type(**params.adj_highschool)
+        highschool_type.assign_parameters(**params.adj_highschool)
         highschool_type.AgeDstn = calc_stationary_age_dstn(highschool_type.LivPrb, True)
         college_type = deepcopy(dropout_type)
-        college_type(**params.adj_college)
+        college_type.assign_parameters(**params.adj_college)
         college_type.AgeDstn = calc_stationary_age_dstn(college_type.LivPrb, True)
         dropout_type.update()
         highschool_type.update()
@@ -491,14 +487,23 @@ def estimate(options, params):
         economy.center_estimate = center_estimate
         economy.spread_estimate = spread_estimate
         economy.show_many_stats(spec_name)
-        print(f"These results have been saved to ./Code/Results/{spec_name}.txt\n\n")
+        print(f"These results have been saved to ./code/results/{spec_name}.txt\n\n")
 
     return economy
 
 
+class Estimator:
+    def __init__(self, options, parameters):
+        self.options = options
+        self.parameters = parameters
+
+    def estimate(self):
+        return estimate(self.options, self.parameters)
+
+
 if __name__ == "__main__":
-    import Code.calibration as parameters
-    from Code.Options.all_options import all_options
+    import code.calibration as parameters
+    from code.options.all_options import all_options
 
     basic_options = all_options["UseUniformBetaDist"].copy()
     basic_options.update(all_options["DoStandardWork"])

diff --git a/Code/figures.py b/Code/figures.py
@@ -1,6 +1,6 @@
 """
 This module makes some figures for cstwMPC.  It requires that quite a few specifications
-of the model have been estimated, with the results stored in ./Results.
+of the model have been estimated, with the results stored in ./results.
 """
 
 import matplotlib.pyplot as plt
@@ -12,7 +12,7 @@
 my_file_path = os.path.dirname(os.path.abspath(__file__))
 
 
-f = open(my_file_path + "/Results/LCbetaPointNetWorthLorenzFig.txt")
+f = open(my_file_path + "/results/LCbetaPointNetWorthLorenzFig.txt")
 my_reader = csv.reader(f, delimiter="\t")
 raw_data = list(my_reader)
 lorenz_percentiles = []
@@ -27,7 +27,7 @@
 scf_lorenz = np.array(scf_lorenz)
 beta_point_lorenz = np.array(beta_point_lorenz)
 
-f = open(my_file_path + "/Results/LCbetaDistNetWorthLorenzFig.txt")
+f = open(my_file_path + "/results/LCbetaDistNetWorthLorenzFig.txt")
 my_reader = csv.reader(f, delimiter="\t")
 raw_data = list(my_reader)
 beta_dist_lorenz = []
@@ -36,7 +36,7 @@
 f.close()
 beta_dist_lorenz = np.array(beta_dist_lorenz)
 
-f = open(my_file_path + "/Results/LCbetaPointNetWorthMPCfig.txt")
+f = open(my_file_path + "/results/LCbetaPointNetWorthMPCfig.txt")
 my_reader = csv.reader(f, delimiter="\t")
 raw_data = list(my_reader)
 mpc_percentiles = []
@@ -48,7 +48,7 @@
 mpc_percentiles = np.asarray(mpc_percentiles)
 mpc_beta_point = np.asarray(mpc_beta_point)
 
-f = open(my_file_path + "/Results/LCbetaDistNetWorthMPCfig.txt")
+f = open(my_file_path + "/results/LCbetaDistNetWorthMPCfig.txt")
 my_reader = csv.reader(f, delimiter="\t")
 raw_data = list(my_reader)
 mpc_beta_dist = []
@@ -57,7 +57,7 @@
 f.close()
 mpc_beta_dist = np.asarray(mpc_beta_dist)
 
-f = open(my_file_path + "/Results/LCbetaDistLiquidMPCfig.txt")
+f = open(my_file_path + "/results/LCbetaDistLiquidMPCfig.txt")
 my_reader = csv.reader(f, delimiter="\t")
 raw_data = list(my_reader)
 mpc_beta_dist_liquid = []
@@ -66,7 +66,7 @@
 f.close()
 mpc_beta_dist_liquid = np.asarray(mpc_beta_dist_liquid)
 
-f = open(my_file_path + "/Results/LCbetaDistNetWorthKappaByAge.txt")
+f = open(my_file_path + "/results/LCbetaDistNetWorthKappaByAge.txt")
 my_reader = csv.reader(f, delimiter="\t")
 raw_data = list(my_reader)
 kappa_mean_age = []
@@ -82,7 +82,7 @@
 age_list = np.array(list(range(len(kappa_mean_age))), dtype=float) * 0.25 + 24.0
 f.close()
 
-f = open(my_file_path + "/Results/LC_KYbyBeta.txt")
+f = open(my_file_path + "/results/LC_KYbyBeta.txt")
 my_reader = csv.reader(f, delimiter="\t")
 raw_data = list(my_reader)
 KY_by_beta_lifecycle = []
@@ -94,7 +94,7 @@
 KY_by_beta_lifecycle = np.array(KY_by_beta_lifecycle)
 f.close()
 
-f = open(my_file_path + "/Results/IH_KYbyBeta.txt")
+f = open(my_file_path + "/results/IH_KYbyBeta.txt")
 my_reader = csv.reader(f, delimiter="\t")
 raw_data = list(my_reader)
 KY_by_beta_infinite = []
@@ -152,7 +152,7 @@
 plt.show()
 
 
-f = open(my_file_path + "/Results/IHbetaPointNetWorthLorenzFig.txt")
+f = open(my_file_path + "/results/IHbetaPointNetWorthLorenzFig.txt")
 my_reader = csv.reader(f, delimiter="\t")
 raw_data = list(my_reader)
 lorenz_percentiles = []
@@ -167,7 +167,7 @@
 scf_lorenz = np.array(scf_lorenz)
 beta_point_lorenz = np.array(beta_point_lorenz)
 
-f = open(my_file_path + "/Results/IHbetaDistNetWorthLorenzFig.txt")
+f = open(my_file_path + "/results/IHbetaDistNetWorthLorenzFig.txt")
 my_reader = csv.reader(f, delimiter="\t")
 raw_data = list(my_reader)
 beta_dist_lorenz = []
@@ -177,7 +177,7 @@
 beta_dist_lorenz = np.array(beta_dist_lorenz)
 
 
-f = open(my_file_path + "/Results/IHbetaPointLiquidLorenzFig.txt")
+f = open(my_file_path + "/results/IHbetaPointLiquidLorenzFig.txt")
 my_reader = csv.reader(f, delimiter="\t")
 raw_data = list(my_reader)
 beta_point_lorenz_liquid = []
@@ -186,7 +186,7 @@
 f.close()
 beta_point_lorenz_liquid = np.array(beta_point_lorenz_liquid)
 
-f = open(my_file_path + "/Results/IHbetaDistLiquidLorenzFig.txt")
+f = open(my_file_path + "/results/IHbetaDistLiquidLorenzFig.txt")
 my_reader = csv.reader(f, delimiter="\t")
 raw_data = list(my_reader)
 beta_dist_lorenz_liquid = []
@@ -195,7 +195,7 @@
 f.close()
 beta_dist_lorenz_liquid = np.array(beta_dist_lorenz_liquid)
 
-f = open(my_file_path + "/Results/IHbetaPointNetWorthMPCfig.txt")
+f = open(my_file_path + "/results/IHbetaPointNetWorthMPCfig.txt")
 my_reader = csv.reader(f, delimiter="\t")
 raw_data = list(my_reader)
 mpc_percentiles = []
@@ -207,7 +207,7 @@
 mpc_percentiles = np.asarray(mpc_percentiles)
 mpc_beta_point = np.asarray(mpc_beta_point)
 
-f = open(my_file_path + "/Results/IHbetaDistNetWorthMPCfig.txt")
+f = open(my_file_path + "/results/IHbetaDistNetWorthMPCfig.txt")
 my_reader = csv.reader(f, delimiter="\t")
 raw_data = list(my_reader)
 mpc_beta_dist = []
@@ -216,7 +216,7 @@
 f.close()
 mpc_beta_dist = np.asarray(mpc_beta_dist)
 
-f = open(my_file_path + "/Results/IHbetaDistLiquidMPCfig.txt")
+f = open(my_file_path + "/results/IHbetaDistLiquidMPCfig.txt")
 my_reader = csv.reader(f, delimiter="\t")
 raw_data = list(my_reader)
 mpc_beta_dist_liquid = []
@@ -276,43 +276,43 @@
 plt.show()
 
 
-f = open(my_file_path + "/Results/SensitivityRho.txt")
+f = open(my_file_path + "/results/SensitivityRho.txt")
 my_reader = csv.reader(f, delimiter="\t")
 raw_data = list(my_reader)
 rho_sensitivity = np.array(raw_data)
 f.close()
 
-f = open(my_file_path + "/Results/SensitivityXiSigma.txt")
+f = open(my_file_path + "/results/SensitivityXiSigma.txt")
 my_reader = csv.reader(f, delimiter="\t")
 raw_data = list(my_reader)
 xi_sigma_sensitivity = np.array(raw_data)
 f.close()
 
-f = open(my_file_path + "/Results/SensitivityPsiSigma.txt")
+f = open(my_file_path + "/results/SensitivityPsiSigma.txt")
 my_reader = csv.reader(f, delimiter="\t")
 raw_data = list(my_reader)
 psi_sigma_sensitivity = np.array(raw_data)
 f.close()
 
-f = open(my_file_path + "/Results/SensitivityMu.txt")
+f = open(my_file_path + "/results/SensitivityMu.txt")
 my_reader = csv.reader(f, delimiter="\t")
 raw_data = list(my_reader)
 mu_sensitivity = np.array(raw_data)
 f.close()
 
-f = open(my_file_path + "/Results/SensitivityUrate.txt")
+f = open(my_file_path + "/results/SensitivityUrate.txt")
 my_reader = csv.reader(f, delimiter="\t")
 raw_data = list(my_reader)
 urate_sensitivity = np.array(raw_data)
 f.close()
 
-f = open(my_file_path + "/Results/SensitivityMortality.txt")
+f = open(my_file_path + "/results/SensitivityMortality.txt")
 my_reader = csv.reader(f, delimiter="\t")
 raw_data = list(my_reader)
 mortality_sensitivity = np.array(raw_data)
 f.close()
 
-f = open(my_file_path + "/Results/SensitivityG.txt")
+f = open(my_file_path + "/results/SensitivityG.txt")
 my_reader = csv.reader(f, delimiter="\t")
 raw_data = list(my_reader)
 g_sensitivity = np.array(raw_data)

diff --git a/cstwMPC.ipynb b/cstwMPC.ipynb
@@ -30,18 +30,16 @@
    "source": [
     "# This cell does some standard python setup!\n",
     "\n",
-    "import os  # Tools for navigating the filesystem\n",
+    "import code.calibration as parameters\n",
     "import warnings  # The warnings package allows us to ignore some harmless but alarming warning messages\n",
+    "from code.calibration import SCF_wealth, SCF_weights\n",
+    "from code.estimation import estimate\n",
     "\n",
     "# Import related generic python packages\n",
     "import matplotlib.pyplot as plt  # Plotting tools\n",
     "import numpy as np\n",
     "from HARK.utilities import get_lorenz_shares\n",
     "\n",
-    "import Code.calibration as parameters\n",
-    "from Code.calibration import SCF_wealth, SCF_weights\n",
-    "from Code.estimation import estimate\n",
-    "\n",
     "warnings.filterwarnings(\"ignore\")\n",
     "\n",
     "\n",

diff --git a/cstwMPC.py b/cstwMPC.py
@@ -24,18 +24,16 @@
 # %% code_folding=[]
 # This cell does some standard python setup!
 
-import os  # Tools for navigating the filesystem
+import code.calibration as parameters
 import warnings  # The warnings package allows us to ignore some harmless but alarming warning messages
+from code.calibration import SCF_wealth, SCF_weights
+from code.estimation import estimate
 
 # Import related generic python packages
 import matplotlib.pyplot as plt  # Plotting tools
 import numpy as np
 from HARK.utilities import get_lorenz_shares
 
-import Code.calibration as parameters
-from Code.calibration import SCF_wealth, SCF_weights
-from Code.estimation import estimate
-
 warnings.filterwarnings("ignore")
 
 

diff --git a/do_custom.py b/do_custom.py
@@ -2,8 +2,8 @@
 This module runs a custom model specification, with all options specified by the user.
 """
 
-import Code.calibration as parameters
-from Code.estimation import estimate
+import code.calibration as parameters
+from code.estimation import estimate
 
 param_name = "DiscFac"  # Which parameter to introduce heterogeneity in
 dist_type = "uniform"  # Which type of distribution to use

diff --git a/do_max.py b/do_max.py
@@ -0,0 +1,27 @@
+"""
+This file will run the two main specifications of the cstwMPC project: FBS-style
+aggregate shocks, perpetual youth, matching net worth.  Will run both beta-point
+and beta-dist versions.
+"""
+
+import code.calibration as parameters
+from code.estimation import estimate
+from code.options.all_options import all_options
+
+basic_options = all_options["UseUniformBetaDist"].copy()
+basic_options.update(all_options["DoStandardWork"])
+
+# Run beta-point model
+
+point_options = basic_options.copy()
+point_options.update(all_options["LCSpecPoint"])
+
+estimate(point_options, parameters)
+
+# Run beta-dist model
+
+dist_options = basic_options.copy()
+dist_options["do_combo_estimation"] = True
+dist_options.update(all_options["LCSpecDist"])
+
+estimate(dist_options, parameters)