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It's not a built-in feature. Note that the residuals that we compute aren't regime specific but rather an expected value using the regime probabilities. This accords with the notion that you never know what regime you are in. For the computation that you'd describe, you'd have to use the coefficient...

Hard to say. The switching objective is very different from the simple minimum sums-of-squares that you get in the standard case.

What is regime specific in your model?

My 2 minute look at the De Jong and Mackinnon (1988) paper suggests that if the above is indeed what you want, you can get it by saving the one-step ahead and filtered state covariances and doing a recursion. This is based on a quick look so I may be wrong, but it looks as though it's possible from ...

The former.

Do you want cov(state(t|T), state(T|T))?

I haven't looked at this in a while, but off the top of my head, I can't think of an easy way to get that one from what we provide you. I'll have to take a look at the papers again.

I don't have the time to test this right now, but it should work.

All that the variance equation is doing is evaluating the expression to get the appropriate variance for a give observation.

I've just posted a fix for this bug. I've also taken the opportunity to add a bit of additional limited early error checking for some specification errors so that the dialog with be reentrant. The fix will appear in the next patch.

Thanks again for your patience.

Thanks for the report. We will take a look.

I'm sorry, but that is correct.

I'm afraid that I don't understand the question.

Ignore him. It's on my list.

"Survey weights" is ambiguous, but I'm assuming you mean frequency weights or the equivalent... I hate to be the bearer of bad news but while there are many tasks you might want to do where you can easily do things to approximate frequency weights, the distributions calculations are going ...

The convention that we use is as described in the documentation as P(i, j) = Pr(S_t = j | S_t-1 = i). . P21 is Pr(S_t = 1 | S_t-1 = 2), or from 2 -> 1. . So if I am thinking clearly this morning, in your case, increases in UNEMPAPR16 lowers the probability of transferring from 2->1, and increases th...

Fair enough on the suggestion for the docs. The idea seemed straightforward when written, but I can see why it might not be obvious. For now, let's try this a different way. Rather than me try to figure out your particular model, here is the basic idea for getting the correlation timing .(Note that ...

Can you be more specific about the process you want to impose for your states? How do they evolve?

I must admit that I don't entirely understand what you are trying to do with your calculation. The following shows the equivalence between post-multiplying the data by the eigenvectors and the score series created using normalize loadings scaling. Note that as expected the average sum of the squared...