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st: missing standard errors in ML estimation

From   Yi-Yi Chen <>
Subject   st: missing standard errors in ML estimation
Date   Tue, 9 Mar 2010 01:31:23 +0800

Dear All,

I wrote my own log-likelihood function for a fairly complicated model
and estimated it using Stata's -ml max- (Stata 10). A simulation study
(using generated data) showed that the coefficients are estimated
pretty well. However, I noticed that, for each replication, Stata
almost always returns missing values for some or all of the
coefficients' standard errors. Why is that?

Except for this anomaly, there is no other obvious problem. There is
no "flat" or "none concave" type of warning messages, nothing. The
only thing that I might add is that because the model's error
structure is complicated and a Gauss-Hermite algorithm is used in the
ML function, I choose a generous convergence criterion (ltol(1e-2)
tolerance(1e-2) ) in the estimation. If I tighten it up, the model
often has difficulty declaring convergence.

If there is any problem in the Hessian matrix, wouldn't it lead to
"flat" or "none concave" type of error message? Anyway, what may have
caused the problem and how may I work around it?

Thank you!

Y. Chen

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