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Sorry, there is nothing built-in to compute them.

Yes, what you are doing makes perfect sense. I wasn't quite sure what the "mean_" variables were doing since I hadn't really looked at the original specification, and think I just mushed all of the variables together in my mind. Sorry to worry you.

Took a quick look at the state specs. Just double checking that you did want to extend Diebold and Li by specifying the states as a VAR(2).

There are many standard errors in panel data models so I'm answering this with respect to the simplest form. The basics apply to the other methods as well. In the usual way of computing fixed effects estimators and the associated standard errors, we orthogonalize all of the data with respect to the ...

Been down a few rabbit holes trying to track this down, but I just noticed that the signal equation specifications in your sspace object don't match the Diebold and Li paper and that they differ from the R specifications. I'm not sure that this is the sole reason for the issues you are seeing, but I...

No. This is easy to do by hand for a specific case but can be tricky to implement in our general interface. It probably could have been done with a fair amount of effort, but our sense at the time, for better or worse, was that our energy would best be spent elsewhere. But I could certainly be convi...

What exactly are you doing when it closes? How did you try to run the test?

The extended Kalman filter is not supported in EViews.

EViews doesn't do anything special with estimation of a VAR in a panel workfile. We simply take all of the lags and run on the stacked data. So the number of observations in different cross-sections will be different, but from the point of view of the estimator, T = sum_i T_i* where T_i* are the num...

You are correct that it does not allow for solution of the model without prior specification of the underlying observed states and in that respect it is limiting. In a sense it is akin to embedding an equation object in a model without providing exogenous series. In the event that one does generate ...

Try the "propnas" option which produces NAs for partially observed periods.

A fix has been posted.

Sorry I missed seeing this question.

The CSD test is available on a series in a panel workfile. Simply open the series, select View/Cross-section Dependence Test.

If you forecast without the "i" option, you'll get the predicted probabilities.

White period allows for intra cross-section (between period) correlation. White cross-section allows for intra period (between cross-section) correlation.