I want to forecast calibrated model, when some variables are unobservable. So it is needed to run a smoother and then forecast.
In the mod file with no estimated estimated_params block I wrote:

varobs x xx;
estimation(datafile=xxx,mode_compute=0, smoother, forecast=zz);

But I’ve got an error. There is an error also when I remove forecast. The mod file works correctly with removed varobs and estimation blocks. I do not know where the problem is. Please, help me with the problem, this is urgent.
Thank you!

But is it possible to forecast smoothed variables with calibrated parameters? With calib_smoother, I know you can forecast filtered variables. But can you forecast smooth variables (with no estimated estimated_params block)?

I tried it, no error, but also no oo_.forecast

estimation(datafile=pickupdata0, mode_compute=0, smoother, forecast=7, first_obs=1) inf_obs y i pi;

Oh yeah, I see it…because we use all the data. Thanks! I thought though that the original question was trying to forecast smoothed variables using a calibrated model…but I see why that reasoning is wrong.

Actually, I wanted to do the following…

Given data y = {y_1,y_2,y_3...y_{20}...y_T}

Estimate the model parameters with y

Then forecast say \hat{y_{21}}, \hat{y_{22}}, \hat{y_{23}},\hat{y_{24}},\hat{y_{25}} (5 steps ahead sequentially).

And compare forecast in point 3 above with data {y_{21}}, {y_{22}}, {y_{23}},{y_{24}},{y_{25}}.

I have been able to do that using filtered variables and forecast of filtered variables via calib_smoother

My problem: Filtered variables although have the same dynamics as original variables, the scale is different after running my mod file in dynare. Scale, I mean the y-axis of filtered variables is about 3 to 4 times larger than that in the original data.

My earlier thought: Given that smoothed variables tend to be closer to the original, I wanted to consider it as original (i.e., approximately), and if it were forecastable (i.e., true in-sample forecast), I will take that to approximate \hat{y_{21}}, \hat{y_{22}}, \hat{y_{23}},\hat{y_{24}},\hat{y_{25}} above. But I get why it is not a true in-sample forecast and we can’t do a 5-step ahead forecast (of smoothed variables) from say period 20.

It seems in-sample forecast can only be done with filtered variables and not original variables, right?

Yeah. ‘Closer’, I mean smoothed variables are identical to observed variables.

Oh ok, so if I understand, for a somewhat correctly specified model, the deviation between filtered variables and observed variables should be approximately zero (very small).

The filtered variables are still x-period ahead forecasts. So the deviations from observations should be roughly mean zero, but not on a period by period basis.

In-sample forecast can only be done using filtered variables and not observations or smoothed variables in dynare, yeah?. And here, I cannot consider filtered variables approximately as observations since the two can deviate on a period-by-period basis.

By construction, only the filtered variables are true forecasts for the observables. As I said, the fact that the filtered values are so different from the realizations suggests there is something seriously wrong.