Fix AdjustPrb for discrete portfolio choice. #391
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MridulS
reviewed
Sep 21, 2019
| @@ -244,9 +242,6 @@ def _calcwFunc(AdjustPrb, AdjustCount, ShareNowCount, vFunc_adj, CRRA): | |||
| evVals = AdjustPrb*vFunc_adj[0][ShareIndex](evalgrid) + (1-AdjustPrb)*vFunc_adj[1][ShareIndex](evalgrid) | |||
| with np.errstate(divide='ignore', over='ignore', under='ignore', invalid='ignore'): | |||
| evValsNvrs = utility_inv(evVals,gam=CRRA) | |||
| evVals = np.insert(evVals, 0, 0.0) | |||
| evValsNvrs = np.insert(evVals, 0, 0.0) | |||
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Yeah, this was exactly where the error was popping in from. If there was a divide by zero error (in case of AdjustPrb < 1) which was ignored by np.errstate there was a mismatch between the length of evVals and evValsNvrs
shaunagm
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Fix #389
So there were some leftover lines from a different way of constructing the re-curving value function interpolation.
A thing I noticed while trying this out to be sure it worked. If you set the probability to 0.5 and look at the share of periods where the agents can adjust, it'll be closer to 0.6 than 0.5. It could maybe be interesting to ask students to calculate the empirical share, and comment. (The answer is that when a new agent is replacing an old one whose lifecycle ended or who died, a new agent will always get the choice of their initial portfolio which implies a slightly higher "can adjust" ratio than 0.5).