Comments (2)
Done for IndShock model, but not for other classes (they have placeholder methods that throw a not implemented error). Has option to use current income distribution discretization or use an absurdly dense one. Doing the latter reveals that our solution method is poor near the lower bound of m in some circumstances, for reasons that are too long to explain here.
Only works for one period infinite horizon models right now, must extend this. Also should write something to "ignore" the Euler error when c_t is on the constrained portion; first order condition doesn't apply here, "Euler error" is meaningless.
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The first order condition == the arbitrage equation in Dolo
The Euler error is the value of the right hand side of the equation (not set equal to 0).
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Related Issues (20)
- Align 'new python model' format with recent language research outcomes
- Configurable mortality and aging in an AgentTypeMonteCarloSimulator HOT 1
- YAML config file to New Python Model Format (NPMF)
- NPMF for ConsIndShock HOT 4
- utilities.py: metadata bug in make_figs() HOT 7
- IndexError when solving ConsPortfolioModel HOT 2
- PermShk's are not random enough HOT 2
- We should not use both `seed` and `RNG`. HOT 4
- ImportError: cannot import name 'generated_jit' from 'numba' HOT 4
- Module not found... numpy? HOT 6
- calc_stable_points is being run when it shouldn't be
- ConsBequestModel and related example notebooks HOT 1
- Improve contributor guide with respect to API documentation
- What is update? How many terminal solutions? HOT 8
- Inheritance... HOT 4
- Proposed constructed input framework HOT 11
- Make sure our links are updated to the new doc website
- Should pytest be a requirement? HOT 3
- IndShockConsumerType.calc_jacobian() changes cFunc_terminal_ HOT 4
- Why does PerfForesightLabeled inherit from ConsIndShock? HOT 7
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