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From |
"Scott Merryman" <[email protected]> |

To |
<[email protected]> |

Subject |
st: Re: ADDENDA |

Date |
Thu, 30 Oct 2003 17:35:46 -0600 |

----- Original Message ----- From: "Clive Nicholas" <[email protected]> To: <[email protected]> Sent: Wednesday, October 29, 2003 12:09 AM Subject: st: ADDENDA > Scott, > > Also, what's wrong with fitting a random-effects logit model? My models > are identified at the respondent-level (as I think practically said just > now!). > > C. > Nothing (as far as I know). Perhaps, the expert you were referring to had Maddala on her mind. The following quote is from Greene: "Consider, as well, Maddala (1987) who states 'By contrast, the fixed effects probit model is difficult to implement computationally. The conditional ML method does not produce computational simplifications as in the logit model because the fixed effects do not cancel out. This implies that all N fixed effects must be estimated as part of the estimation procedure. Further, this also implies that, since the estimates of the fixed effects are inconsistent for small T, the fixed effects probit model gives inconsistent estimates for B as well. Thus, in applying the fixed effects models to qualitative dependent variables based on panel data, the logit model and the log-linear models seem to be the only choices. However, in the case of random effects models, it is the probit model that is computationally tractable rather than the logit model.' (Page 285) While the observation about the inconsistency of the probit fixed effects estimator remains correct, as discussed earlier, none of the other assertions in this widely referenced source are correct. The probit estimator is actually extremely easy to compute. Moreover, the random effects logit model is no more complicated than the random effects probit model. (One might surmise that Maddala had in mind the lack of a natural mixing distribution for the heterogeneity in the logit case, as the normal distribution is in the probit case. The mixture of a normally distributed heterogeneity in a logit model might seem unnatural at first blush. However, given the nature of 'heterogeneity' in the first place, the normal distribution as the product of the aggregation of numerous small effects seems less ad hoc.)" William Greene, 2001 Fixed and Random Effects in Nonlinear Models, page 14, available at: http://pages.stern.nyu.edu/~wgreene/panel.pdf * * 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/

**Follow-Ups**:**st: Greene***From:*"Clive Nicholas" <[email protected]>

**References**:**st: R-SQUARED AND XTGEE***From:*"Clive Nicholas" <[email protected]>

**st: Re: R-SQUARED AND XTGEE***From:*"Scott Merryman" <[email protected]>

**st: ADDENDA***From:*"Clive Nicholas" <[email protected]>

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