I am using diffuse_filter
in estimation.
After estimation I did these two procedures independent.

I got predictive density using forecasts saved across draws in the metropolissubfolder in the FILENAME_forc_point*.matfiles. It looks good.

When I retrieve a spacestate representation using output after estimation,
oo_.dr.ghx
,oo_.dr.ghu
andM_.Sigma_e
and I forecast the statespace model using MonteCarlo method the density predictive is some explosive. In this forecast I am using ssm function of matlab which use standard Kalman filter (“no diffuse”) to get the initial state means and covariances. So I suspect the explosiveness is dued to using standard filter.
My question is: ¿should I use diffuse filter to compute the initial state means and covariances to initialize forecast (and respectively, should I use diffuse filter to compute forward recursions too)?
Thanks a lot.