Re: st: How to save a hessian matrix for post-estimation analysis?

[Gordon Hughes <G.A.Hughes@ed.ac.uk>]

Sorry about the delay in responding.  Unfortunately, the real answer 
to your question is that it all depends on what you are trying to 
do.  I think that the best advice is to try and remove the source of 
the degeneracy - i.e. remove collinear variables or whatever.  If 
there is some reason why this can't be done (I can't think of an 
obvious example), then the generalised inverse is pretty robust so 
that the non-missing diagonal elements might be used for tests on the 
relevant coefficients.

I would give one warning.  Degeneracy in the variance-covariance 
matrix is commonly a sign that the maximisation has failed - i.e. the 
likelihood function does not have a single local maximum and the 
gradient procedure has headed off to a region that is infeasible or 
degenerate for some reason.  Any inferences based on estimates of 
this kind are worthless.  This brings us back full circle to the 
question of why you have degenerate variance-covariance matrix.

Gordon Hughes
g.a.hughes@ed.ac.uk

============================

Dear Gordon,

Thanks for the answer. It seems that there is no easy way to use the 
official command, e.g. "logit", "mprobit", etc., and save the original hessian.

By the way, I wonder that, if the negative hessian is singular or not 
positive definite, is there any drawback to using the generalised 
inverse hessian? I know that some standard errors in the output will 
be labelled with a dot, which means we can't do the statistical tests 
for those predictors. How about the other standard errors appearing 
as usual? We can use them to do the test, but will them cause invalid results?

Thanks for the help.

Best Regards,

Chi-lin Tsai


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