EViews.com

That's an interesting model. Let me see if I can take a crack at this. First off, in the DEPVAR1 equation you define the error E1 with a variance specification and reuse the error E1 in the DEPVAR2 equation. I must admit that while I think your text specification is fine, I would probably write the ...

Thanks for putting that together. It helps for us have a common set of data with which to work. It is unsuprising that you were unable to replicate the results from the in-built EViews 12 scaled elastic net as the documentation is frustratingly silent about the approach. I apologize for that. As you...

Can you post your workfile?

Sorry. Just saw this.

The fix is in the current patch, dated Feb 5. I believe it will be pushed in the next day or so.

Hi. Thanks for your question. The engine for elastic net estimation was completely revamped between the two versions. In particular, we moved to a coordinate descent algorithm with warm start path estimation which offers considerable computational efficiencies and more importantly, numeric stability...

Sorry for the inconvenience. We'll get a fix out soon.

No promises, but asking for clarification... How would you want the gains to be saved? As a matrix with rows representing observations? And each row with a vec of the gain matrix? As one matrix for each period? Trying to think about what an interface would look like that provides benefits over the s...

Could you please post your workfile so we are looking at the same thing (and to make discussion easier). Thanks. My first instinct is that there is a difference in the timing between the covariance between the states and the signals in the two cases. The second case has a correlation between the DEP...

From the point of VAR analysis, a VAR with no lags isn't particularly useful. It is simply a system of equations. As such, much of the standard VAR toolkit like impulse response analysis simply isn't useful in that case. But some of the methods that we support, like Markov switching, may be of inter...

Hi. To be honest, I'm not certain that I understand the proposed exercise, in particular, what you mean by the "projected change" and what distributive property you want your results to follow.

They scale the regressors by the L1 and L2 norms of the data, respectively, prior to estimation. So the penalties on parameters will be on coefficients that are for scaled data. The coefficients are transformed back to original scale for reporting.

Hope that this answers your question.

Are your lag coefficients regime-specific? If they are not, then the impulse-responses are not regime-specific. If they are, then you will see a spool with the IRs for each regime.

I am sorry, but no.

Sorry, just saw this. Yes. Please provide, along with as much information as possible. Thanks.

It's up. Take a look.