Allow for dependence on discrete choices in stochastic transitions #53
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src/lcm/example_models_stochastic.py
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| @@ -9,7 +9,7 @@ | |||
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| def utility(consumption, working, health, partner, delta, gamma): # noqa: ARG001 | |||
| return jnp.log(consumption) + (gamma * health - delta) * working | |||
| return jnp.log(consumption + 1) + (gamma * health - delta) * working | |||
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Why is this change necessary? The constraint consumption > 0 should be handled elsewhere, if possible. Not clear to me why consumption > -0.99999 is the easier thing to handle?
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Good point. Currently, we only have a proper constraint for consumption < wealth. I changed the grids of the model for consumption and wealth to both start at 1 (in principle, any positive number would work).
src/lcm/example_models_stochastic.py
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| "partner": jnp.array([[1.0, 0], [0.0, 1]]), | ||
| "partner": jnp.array( | ||
| [ | ||
| [[[0, 1.0], [1.0, 0]], [[0, 1.0], [0.0, 1.0]]], |
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Do we rely on these somewhere? If possible, numbers like [[0.9, 0.1], [0.05, 0.95]] or so would be even better (=more instructive for people using this as documentation). Same for health; beta could be 0.95 ?
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Very good point. We cannot change the degenerate distribution for the partner shock because the tests use this, however, I added more comments and made the distribution of health more realistic.
In this PR, I adjust the stochastic state transitions to allow for dependence on discrete choice variables.
Specifically, I
next_weights(see changes insrc/lcm/model_functions.py)