I’m calibrating to log-hp filtered second moments. I’m aware that in order for the theoretical moments to be comparable with log-hp filtered data moments, it’s recommendable to also hp filter the (logs) model with `stoch_simul(hp_filter=1600, order=1)`

, which compute theoretical hp-filtered following the outline of Uhlig (2001) according to the Dynare manual (4.6).

Now, when performing moment-matching in my model and following the previous process, I think that applying the hp-filter to the model is difficulting the matching of some moments. For instance, my data gdp first-order autocorrelation is about 0.85, when I use a first-guess calibration parameter vector the no-hp filtered theoretical moment for \rho(gdp_{t},gdp_{t-1}) is about 0.9, and when I hp-filter the theoretical moment is about 0.7. The thing is that for this second case, when calibrating the model struggles a lot for increasing this autocorrelation to 0.84, where the identified parameter (which is the autocorrelation parameter for TFP ar(1) process) wants to get values too close to a unit-root, which is wrong since my model does not contemplate unit root to TFP shocks.

Also, with other moments the model struggles to get correct or close to correct theoretical moment values. I’d appreciate if you could give me some advice in how to improve my calibration by moment matching. I think I’m close, I’ve tried trying different initial-guess parameter values, also I’ve dropped from calibration some moments that do not improve its distance from the data ones after many iterations.

Some comments would be appreciated. Thanks!

PD: Also I would want to check if this mentioned Uhlig (2001) reference for computing hp-filtered theoretical moments is this same one. Thanks.