remove old population parameters cruft, fixes #142

simulate/parameters.py

@@ -4,33 +4,13 @@
 from HARK.distribution import Uniform
 from xarray import DataArray
 
-dist_params = {
-    "CRRA": {"bot": 2, "top": 10, "n": 3},  # Chosen for "interesting" results
-    "DiscFac": {"bot": 0.936, "top": 0.978, "n": 2},  # from CSTW "MPC" results
-}
-
 # Get empirical data from Sabelhaus and Song
 ssvp = sabelhaus_song_var_profile()
 
 # Assume all the agents are 40 for now.
 # We will need to make more coherent assumptions about the timing and age of the population later.
-# Scaling from annual to quarterly
 idx_40 = ssvp["Age"].index(40)
 
-### parameters shared by all agents
-agent_parameters = {
-    "aNrmInitStd": 0.0,
-    "LivPrb": [0.98**0.25],
-    "PermGroFac": [1.01**0.25],
-    "pLvlInitMean": 1.0,
-    # initial distribution of permanent income
-    "pLvlInitStd": 0.0,
-    "Rfree": 1.0,
-    "TranShkStd": [ssvp["TranShkStd"][idx_40] / 2],
-    # Adjust non-multiplicative shock to quarterly
-    "PermShkStd": [ssvp["PermShkStd"][idx_40] ** 0.25],
-}
-
 ### new dictionary for new Agent Population
 
 agent_population_params = {
@@ -40,6 +20,7 @@
     "pLvlInitMean": 1.0,
     "pLvlInitStd": 0.0,
     "Rfree": 1.0,
+    # Scaling from annual to quarterly
     "TranShkStd": DataArray([ssvp["TranShkStd"][idx_40] / 2], dims="age"),
     "PermShkStd": DataArray([ssvp["PermShkStd"][idx_40] ** 0.25], dims="age"),
 }