Dependencies of stochastic next functions can only be discrete states

[timmens]

Currently, dependencies of stochastic next functions can only be discrete states. In principle, we want to allow for arbitrary arguments. There are a few things that need to be tackled. The current implementation (create_params.py) does:

dependencies = list(
    inspect.signature(model["functions"][f"next_{var}"]).parameters,
)

_check_variables_are_all_discrete_states(
    variables=dependencies,
    variable_info=variable_info,
    msg_suffix=(
        f"The function next_{var} can only depend on discrete state variables."
    ),
)

What we probably should do:

We don't need the list of arguments here, but the root nodes in a dag that contains the next function and all auxiliary functions the user might have provided. Another workaround would be to build a next function that only depends on states and choices via dags and inspects its signature.

What remains to be done:

Extend the logic to continuous variables.

[hmgaudecker]

Extend the logic to continuous variables.

Not sure this is first order. First thing to check would be whether there is anything in the literature doing this.

Another thing might be variables that are choices today, which become states next period (say, employment status). Would that work already?

[hmgaudecker]

Another thing might be variables that are choices today, which become states next period (say, employment status). Would that work already?

Just to have an answer should we come back to it: This does work since #53.