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st: Greene

From   "Clive Nicholas" <[email protected]>
To   [email protected]
Subject   st: Greene
Date   Fri, 31 Oct 2003 01:37:29 -0000 (GMT)


Cheers for that. I actualy have that chapter, but I must have missed these
quotes in amongst the (generally) impenetrable sea of equations.

By the way, she was a he!! *lol* :-)


> ----- 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:
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Politics Building,
School of Geography, Politics and Sociology,
University of Newcastle-upon-Tyne,
NE1 7RU,
United Kingdom.
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