I have tried using the new features by Willy Mutschler for non-linear identication within Dynare.
When running non-linear estimation, We have to inlcude measurement errors in the estim_param block and try to recover the standard deviation of the error.
Should I include measurement errors within the model block when using the non-linear identification toolbox?
In the linear case, I usually include measurement errors within the model block to check whether they allow to help identify additional parameters - as for instance, shown in the complementary code to https://www.researchgate.net/publication/333420538_The_effect_of_observables_functional_specifications_model_features_and_shocks_on_identification_in_linearized_DSGE_models ).
Would this operation have the same meaning in a non-linear context?
I’ll try to explain better myself …
Beside solving issues of non-identification due to parameters simplifying out from the solution of first-order approximations, higher-order approximations help solving issues of weak identification by increasing the curvature of the likelihood function for some target parameters.
However, compared to the initial model in order to run non-linear estimation we have to include measurement errors.
(My understanding is we include measurement errors in a non-linear estimation process to also provide a distribution to sample particles, initialize algorithms, helping with issues of stochastic singularity.)
I believe this is also helping outperforming on linear estimation (without measurement errors).
So, I guess I should hope the estimated standard deviation for measurement errors should be quite small in order to rely on the truethfullness of my estimated parameters. Am I right?
Many thanks in advance for your help.