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

To |
statalist@hsphsun2.harvard.edu |

Subject |
Re: st: Opinions on fractional logit versus tobit - prediction and model fit |

Date |
Fri, 3 Apr 2009 08:57:18 -0500 |

On 4/2/09, Eva Poen <eva.poen@gmail.com> wrote: > - Although it appears to be a very elegant solution, some people say > that FLM is not well suited for problems with a lot of zeros or ones; > for example, Maarten Buis said so in this post (but didn't provide a > reference): http://www.stata.com/statalist/archive/2007-07/msg00786.html > If someone knows any references where this is discussed, I'd be > grateful to receive them. If you have figured out -gllamm-, then you might be able to use it to set up a mixture/zero-inflation model with two-point distribution of the latent variable, using -ip(f) nip(2)- options for the relevant part of the model. I would probably be more convinced if you had full panels that consist of zeroes, and other panels that have a mixture of 0s and non-zeroes, rather than each panel having 5 zeroes and one or two non-zeroes, since zero-inflation models are essentially stating that an individual is either in "don't-do-it" class with zero outcome, or "do-it-sometimes" class with zero outcomes coming in a random way along with non-zeroes. See -zip- for a canned routine doing this in official Stata. And btw it might be worth looking at -xt[me]poisson- if your data are integers. See if interpreting your dependent variable as a count is at least an approximately reasonable interpretation in your application. > - I am getting sensible estimates for the random effects with the > tobit approach, and not so sensible ones with FLM. In fact, FLM > estimates two of the three to be zero. Is this a sign of my model > being incorrectly specified, or could it be a sign of FLM not handling > the zeros and ones very well? As far as I know (and you should not over-rely on this :)), it is the tobit model that is usually behaving in a weird way, as it is quite fragile to the violations of normality assumptions. So I probably wouldn't put too much value into this kind of comparison; in all likelihood, BOTH models are misspecified, you just need to find the one that is more reasonable than the other :)). Dig for George Box's quote on this! -- Stas Kolenikov, also found at http://stas.kolenikov.name Small print: I use this email account for mailing lists only. * * For searches and help try: * http://www.stata.com/help.cgi?search * http://www.stata.com/support/statalist/faq * http://www.ats.ucla.edu/stat/stata/

**Follow-Ups**:**Re: st: Opinions on fractional logit versus tobit - prediction and model fit***From:*Eva Poen <eva.poen@gmail.com>

**References**:**st: Opinions on fractional logit versus tobit - prediction and model fit***From:*Eva Poen <eva.poen@gmail.com>

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