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Trubador's answer is a good one. I will merely make two additional points: 1. Where you evaluate the LATENT in trubador's expressions matters since the derivatives of the probabilities are nonlinear functions (@dnorm). Often people will evaluate at mean values for X (which I'm not particularly crazy...

It's actually writing a message to the statusline, but it's getting cleared too quickly (though perhaps you can read it quickly before it disappears ). I'll put this on the list of things to look at when we get a chance.

If, in the examples given, you take the distribution graph and change the "Legend labels:" option from "Default" to "Detailed", you will get a lot more identifying information. I think this will give you what you want.

There are a number of goodness-of-fit statistics for limited dependent variables models (of which Poisson ML is one) that are on our list of things to look at...Suggestions regarding which ones people would like to see are always welcome.

At the moment, EViews doesn't have built-in procedures for variance ratio tests. (I will note that this is a feature that is on our list of things to look at for upcoming versions of EViews.) For now, however, it is not particularly difficult to compute the moments for the test statistic using the @...

How are you trying to estimate the HACV for panel data in EViews? It really isn't part of the standard suite of options...

Neither of your state variables has a coefficient on it so I'm not certain how interacting with a dummy variable affects anything (unless all you want to knock out a state for part of the sample). In any event, all you need to do is to multiply the state by the dummy in the signal equation.

It might be worth trying to replace the !mlog2pi replacement variable with a scalar expression. It's possible that there has been a slight changing in the parsing... You'd replace the line reading !mlog2pi = 3*log(2*@acos(-1)) with the statement scalar mlog2pi = 3*log(2*@acos(-1)) and then in the lo...

As with other nonlinear estimation in EViews, the state space routines take starting values from the workfile. There is no guarantee of global concavity to the likelihood functions so that optimal solutions will be starting value dependent. Moreover, it is quite possible (and as you've noted, it oft...

The description in the manual was a general statement about the uses of the Kalman filter. Unfortunately, the specifications allowed in the EViews state space object do not yet support implementing Markov switching, which requires a somewhat specialized setup for the filter. Markov switching is on o...

From the manual...(p. 388-390) "Each state equation must be linear in the one-period lag of the states." "State equations may contain exogenous variables and unknown coefficients and me nonlinear in these elements." "Signal equations must be linear in the contemporaneous sta...

Yes.

The ufactor (unconditional factor (17.27) in Wooldridge) should be 0.645, which it is when I run it. If you want also want the cfactor (conditional factor (17.23) in Wooldridge) you'll have to run the modified version below. I get 0.452 which is a bit off from Wooldridge's reported 0.451, but I thin...

Got it... What you are doing when you perform the forecast (or equivalently, a model solve) is to compute the xi'b at every observation i of the forecast (or model solution) sample. You can get your mean value by simply taking the mean of the forecasted xb values: Since you mention the Wooldridge bo...

HI Glenn, I have tried as you suggested and I get a series which I assume are cumulative probabilities for each forecast. What do I do with this series in order to get the single number adjustment factor? Regards, Boyd Boyd, I'm not certain of the context in which you ask this last question. What e...