These command solve the problem where agents think they know perfectly the
future (they behave as in perfect foresight), but make expectation errors.
Hence they can potentially be surprised in every period, and their expectations
about the future (incl. the final steady state) may change.
Currently the sequence of information sets needs to be passed through a CSV
file. Another interface may be added in the future.
The algorithm uses a sequence of (true) perfect foresight simulations (not
necessarily as many as there are periods, because if the information set does
not change between two periods, there is no need to do a new computation).
There are two possibilities for guess values:
— the default is to use the initial steady state for the simulation using the
first-period information set; then use previously simulated values as guess
values
— alternatively, with the terminal_steady_state_as_guess_value option, use the
terminal steady state as guess value for all future periods (this is actually
what the “true” perfect foresight solver does by default)
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