Perfect Foresight MIT Shock Option Added #1013
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| self.t_cycle[ | ||
| which_agents | ||
| ] = 0 # Which period of the cycle each agent is currently in | ||
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Think about whether you need to add a test for whether the PerfMITShk variable exists, like:
if hasattr(self, 'PerfMITShk'):
before using the variable
| @@ -1577,7 +1578,7 @@ class PerfForesightConsumerType(AgentType): | |||
| MPCmax=1.0, | |||
| ) | |||
| time_vary_ = ["LivPrb", "PermGroFac"] | |||
| time_inv_ = ["CRRA", "Rfree", "DiscFac", "MaxKinks", "BoroCnstArt"] | |||
| time_inv_ = ["CRRA", "Rfree", "DiscFac", "MaxKinks", "BoroCnstArt", "PerfMITShk" ] | |||
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Let's make PerfMITShk something like tolerance which the user does not have the option of causing to be a time_varying thing at all.
Codecov Report
@@ Coverage Diff @@
## master #1013 +/- ##
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Coverage 71.78% 71.78%
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Files 64 64
Lines 9300 9301 +1
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+ Hits 6676 6677 +1
Misses 2624 2624
Continue to review full report at Codecov.
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Documentation/CHANGELOG.md
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| @@ -25,7 +25,7 @@ PR: [#832](https://github.com/econ-ark/HARK/pull/832). See [this forthcoming REM | |||
| * Using Lognormal.from_mean_std in the forward simulation of the RiskyAsset model [#1019](https://github.com/econ-ark/HARK/pull/1019) | |||
| * Fix bug in DCEGM's primary kink finder due to numpy no longer accepting NaN in integer arrays [#990](https://github.com/econ-ark/HARK/pull/990). | |||
| * Add a general class for consumers who can save using a risky asset [#1012](https://github.com/econ-ark/HARK/pull/1012/). | |||
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| Add Boolean attribute 'PerfMITShk' to PerfForesightConsumerType. When true, allows perfect foresight MIT shocks to be simulated. [#1013](https://github.com/econ-ark/HARK/pull/1013). | |||
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Novice users might not realize that adding this to PerfForesightConsumerType also adds it to most of our other consumption models, because most of them inherit from PerfForesightConsumerType. Revise this to be vaguer: "most consumption models" instead of "PerfForesightConsumerType"
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