Create base parameters
@@ -922,19 +922,22 @@ def solveConsPortfolio(solution_next,ShockDstn,IncomeDstn,RiskyDstn,
# Make a dictionary to specify a portfolio choice consumer type
-init_portfolio = init_idiosyncratic_shocks.copy()
-init_portfolio['RiskyAvg'] = 1.08 # Average return of the risky asset
-init_portfolio['RiskyStd'] = 0.20 # Standard deviation of (log) risky returns
-init_portfolio['RiskyCount'] = 5 # Number of integration nodes to use in approximation of risky returns
-init_portfolio['ShareCount'] = 25 # Number of discrete points in the risky share approximation
-init_portfolio['AdjustPrb'] = 1.0 # Probability that the agent can adjust their risky portfolio share each period
-init_portfolio['DiscreteShareBool'] = False # Flag for whether to optimize risky share on a discrete grid only
+init_riskyContrib = init_idiosyncratic_shocks.copy()
+init_riskyContrib['RiskyAvg'] = 1.08 # Average return of the risky asset
+init_riskyContrib['RiskyStd'] = 0.20 # Standard deviation of (log) risky returns
+init_riskyContrib['RiskyCount'] = 5 # Number of integration nodes to use in approximation of risky returns
+init_riskyContrib['ShareCount'] = 25 # Number of discrete points in the risky share approximation
+init_riskyContrib['AdjustPrb'] = 1.0 # Probability that the agent can adjust their risky portfolio share each period
+init_riskyContrib['DiscreteShareBool'] = False # Flag for whether to optimize risky share on a discrete grid only
# Adjust some of the existing parameters in the dictionary
-init_portfolio['aXtraMax'] = 100 # Make the grid of assets go much higher...
-init_portfolio['aXtraCount'] = 200 # ...and include many more gridpoints...
-init_portfolio['aXtraNestFac'] = 1 # ...which aren't so clustered at the bottom
-init_portfolio['BoroCnstArt'] = 0.0 # Artificial borrowing constraint must be turned on
-init_portfolio['CRRA'] = 5.0 # Results are more interesting with higher risk aversion
-init_portfolio['DiscFac'] = 0.90 # And also lower patience
+init_riskyContrib['aXtraMax'] = 50 # Make the grid of assets go much higher...
+init_riskyContrib['aXtraCount'] = 100 # ...and include many more gridpoints...
+init_riskyContrib['aXtraNestFac'] = 1 # ...which aren't so clustered at the bottom
+init_riskyContrib['nMax'] = 50 # Use the same parameters for the risky asset grid
+init_riskyContrib['nCount'] = 100 #
+init_riskyContrib['nNestFac'] = 1 #
+init_riskyContrib['BoroCnstArt'] = 0.0 # Artificial borrowing constraint must be turned on
+init_riskyContrib['CRRA'] = 5.0 # Results are more interesting with higher risk aversion
+init_riskyContrib['DiscFac'] = 0.90 # And also lower patience
