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
  }
 )