updating LUCAS0 parameters based on new macro results

simulate/parameters.py

@@ -88,23 +88,27 @@ def build_population(agent_type, parameters, seed=None, dphm=1500):
 init_portfolio["PermGroFac"] = [1.0]  # no drift in perm income
 # risk free return, set to 1 to focus on equity premium
 init_portfolio["Rfree"] = 1.0
-init_portfolio["RiskyAvg"] = 1.05  # eq_prem is RiskyAvg - Rfree = 0.05
+#init_portfolio["RiskyAvg"] = 1.05  # eq_prem is RiskyAvg - Rfree = 0.05
 init_portfolio["LivPrb"] = [1.0]  # no death
 
 lucas0_agent_population_params = init_portfolio.copy()
 
+# should match quarterly parameters in macro/macro_parameters
 lucas0_agent_population_params.update(
     {
         "cycles": 0,  # issue 186
         # ln(16.35182266895578) from Numerical Buffer Stock notebook using default params
         "aNrmInitMean": 2.79433936935,  # calculated using dashboard from default init_portfolio stats
         # "aNrmInitMean": 6
-        # "LivPrb": [0.98**0.25], -- Should this be adjusted?
-        "PermGroFac": 1.0,
-        "Rfree": 1.0,
+        'PermShkStd': [0.05],
+        'TranShkStd': [0.4],
+        'UnempPrb': 0,
+        'RiskyAvg': 1.0122722344290394, # quarterly, annually ~1.05, for 0.05 eq_prem
+        'RiskyStd': 0.1, # quarterly
+        'aXtraMax': 400,
         ### These are placeholders that will be set when the system is set up.
         "CRRA": 5,
-        "DiscFac": 0.90,
+        "DiscFac": 0.974, # quarterly
         "ex_post": None,  # ex post heterogeneous parameters over which to merge solutions
     }
 )