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From |
"Stas Kolenikov" <skolenik@gmail.com> |

To |
statalist@hsphsun2.harvard.edu |

Subject |
Re: st: xtlogit vs. gllamm. large condition numbers and group variances: what do they mean? |

Date |
Tue, 4 Sep 2007 12:38:23 -0500 |

On 9/4/07, avwilson <avwilson@web.de> wrote: > I have 2 questions, plus 2 follow ups. > 1. is gllamm the right tool to use? And if not, what should I do? There aren't many estimation commands in Stata that cannot be cast in terms of -gllamm- :)). Whether that's the best tool to use is then the question; probably not in your case. > 2. if yes, then should I worry about the condition number and variances? And > what could I do to improve on them? I would say that either of the results suggest absence of the random effects. In both models, the random effects were estimated to be quite close to zero, and -gllamm- essentially reports those as zeroes, while -xtlogit- falls somewhat shy of that. That's a strange result; you would expect that in panel data settings you would see some panel effects, but in your case it appears to be well explained by the demographic variables. Of course another explanation for almost any strange result is that your model is badly misspecified, or that random effect variances somehow fail to be identifiable. The likelihood ratio test reported by -xtlogit- is significant, but that's the effect of a large sample size, large enough to make the variance of less than 1% significant. Is that DHS data, by the way? -- Stas Kolenikov, also found at http://stas.kolenikov.name Small print: Please do not reply to my Gmail address as I don't check it regularly. * * For searches and help try: * http://www.stata.com/support/faqs/res/findit.html * http://www.stata.com/support/statalist/faq * http://www.ats.ucla.edu/stat/stata/

**References**:**st: xtlogit vs. gllamm. large condition numbers and group variances: what do they mean?***From:*"avwilson" <avwilson@web.de>

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