saving the estimated shocks
Posted: Wed Feb 01, 2006 6:54 pm
Dear all,
let me say upfront that I am not a big estimation expert.
I am estimating a model using Bayesian methods and would like the do the following experiment: after estimating the model, I would like to feed the estimated shocks back into the estimated model to run counterfactuals and historical decompositions.
What I am currently doing to implement this is the following:
1) During the estimation, I save the estimated shocks (which are stored by Dynare in the "innov" matrix) adding a line saying "save innov" around line 800 in the file dynare_estimation.m
2) After estimation, I simulate the estimated model replacing the "ex_" matrix inside the simult.m file with the "innov" matrix itself.
My understanding is that the innov are the smoothed shocks and do not replicate the model exactly when I feed them back in the model. In fact, model and actual series do not line up exactly. I was wondering whether there is a way conceptually and practically to do the counterfactuals in the model better than the one I am currently using
Thanks
let me say upfront that I am not a big estimation expert.
I am estimating a model using Bayesian methods and would like the do the following experiment: after estimating the model, I would like to feed the estimated shocks back into the estimated model to run counterfactuals and historical decompositions.
What I am currently doing to implement this is the following:
1) During the estimation, I save the estimated shocks (which are stored by Dynare in the "innov" matrix) adding a line saying "save innov" around line 800 in the file dynare_estimation.m
2) After estimation, I simulate the estimated model replacing the "ex_" matrix inside the simult.m file with the "innov" matrix itself.
My understanding is that the innov are the smoothed shocks and do not replicate the model exactly when I feed them back in the model. In fact, model and actual series do not line up exactly. I was wondering whether there is a way conceptually and practically to do the counterfactuals in the model better than the one I am currently using
Thanks