Also adjust the periods in Simulated_time_series (output of the perfect
foresight solver in the workspace). Note that this dseries object contains the
observations for the initial condition (M_.orig_maximum_lag observations) and
for the terminal condition (M_.orig_maximum_lead observations).
See #1838.
Fix testsuite (wrong file name)
Auxiliary variables were still present in the growth neutrality correction. This
commit remove the auxiliaries, so that the user doesn't need to update the
database with the auxiliary variable definitions.
Also adds integration test.
TODO Check that it works with log unary op
TODO Complete tests by checking that the written evaluate routine works
The taget in PAC equation can be decomposed into an arbitrary number of components (variables
in the VAR auxiliary model).
TODO Iterative OLS estimation (which is not the preferred estimation routine).
TODO Decomposition in the routine evaluating the forecasts for each component.
– multiple “model” and “estimated_params” block are supported
– new “model_options” statement to set model options in a global fashion
– new “model_remove” command to remove equations
– new “model_replace” block to replace equations
– new “var_remove” command to remove variables (or parameters)
– new “estimated_params_remove” block to remove estimated parameters
gen_data.mod creates a datafile called "data.mat"
test_compute_Pinf_Pstar_data.mod creates a datafile called "Data.mat"
There could be a race condition on systems that don't distinguish between upper and lower cases.
Instead of checking everything in one mod file, this commit separates the checks into individual mod files that test:
- whether the translation from matched_moments works
- whether the duplicate moments are found
- whether GMM and SMM both work with different estimated_params blocks.
wip
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)
Partially addresses issue #1680:
- unconditional welfare resorts to dynare++ simulation tools, which shall be updated very soon
TO DO:
- implement a function computing kth-order approximation of simulated moments of y
Contains improvements, in order to recover as much as possible static unobserved (filtered, smoothed, updated, k-step ahead), Variance, State_uncertainty, k-step ahead variances trying to map lagged states onto current ones using pinv(T). This has exceptions (namely lagged shocks which are ONLY used to recover static NON observed variables). this exception is also trapped.
For such extensions we can only recover smoothed variables starting from d+1. Variances CANNOT be recovered for such variables (the smoother gives ZERO.)
Stéphane Adjemian (Ryûk)
Sébastien Villemot
NormannR
Willi Mutschler
Johannes Pfeifer
Marco Ratto