I have a quick question regarding recovering the Hessian variance-
covariance matrix in MLE estimation.
I've got a mixed bernoulli type log-likelihood function that I'm using
the inverselogit(-x-) trick in order to keep Stata from searching for
lambda values less than 0 or greater than 1. Essentially telling it
to estimate x=logit(-lambda-) instead of -lambda- itself.
I know how to ask Stata for the transformed parameter and variance
estimate (That is, transforming the logit(-lambda-) estimate into -
lambda-. -->nlcom invlogit([logitkappa]_cons). What I need are the
covariance terms of the parameters. Essentially the off diagonals of
the Hessian matrix (as if I was estimating -lambda- itself along with
the other parameters. How do I recover that transformation?
Oh, and on a side note, does anyone know why you have to use tricks
like that instead of just
telling Stata not to search for parameters there?
Malcolm
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