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From |
Maarten Buis <maartenlbuis@gmail.com> |

To |
statalist@hsphsun2.harvard.edu |

Subject |
Re: st: mlogit estimation p-value problem |

Date |
Wed, 12 Jun 2013 09:29:21 +0200 |

On Tue, Jun 11, 2013 at 1:54 PM, Andreas Chouliaras wrote: > First of all, why am I not using an ordered logit? Well, I believe > that in an ordered logit, the odds of getting a value for the count > equal to 5, instead of 4, are equivalent to the odds of observing 3 > instead of 2. Close, but not quite: What is being modeled in an ordered logit is the odds of 0 versus more than 0, 1 versus more than 1, etc. > With such a constraint the estimates will be less less > efficient if the odds are not proportional. And I don't think I have a > strong reason why the odds should be proportional in my study. The idea behind models like ordered logit or stereotyped ordered logit is that you use assumptions to borrow information from observations from adjacent categories. This way you gain efficiency. The price is when your assumptions are wrong your results will be biased. So the problem with an ordered logit is not a lack of efficiency but a potential bias. In fact, an ordered logit will tend to be a lot more efficient than a multinomial logit. > Now, regarding the observations of outcome 5: > > bcW has 4 observations for outcome 5, tcW has 3 observations for > outcome 5. I am putting the numbers of observations for outcome 5 for > the other groups > > bcE : 18 tcE : 11 > bcP : 17 tcP : 12 > bcC : 41 tcC : 31 Trying to fit a -mlogit- on such small numbers of observations is guaranteed to lead to hopelessly unstable results. So, given your sample sizes you basically have two imperfect options: either you use models like ordered logit to simplify your model enough such that you get stable resutls or you collect more data and use that extra data to stabelize your results. A potential intermediate solution would be a generalized ordered logit, which can relax the parallel regression assumption for some but not all variables in your model. You can find out more about that here: <http://www.nd.edu/~rwilliam/gologit2/>. However, given the sample sizes you report I don't hold much hope. Hope this helps, Maarten --------------------------------- Maarten L. Buis WZB Reichpietschufer 50 10785 Berlin Germany http://www.maartenbuis.nl --------------------------------- * * For searches and help try: * http://www.stata.com/help.cgi?search * http://www.stata.com/support/faqs/resources/statalist-faq/ * http://www.ats.ucla.edu/stat/stata/

**References**:**st: mlogit estimation p-value problem***From:*Andreas Chouliaras <adhoul@gmail.com>

**Re: st: mlogit estimation p-value problem***From:*David Hoaglin <dchoaglin@gmail.com>

**Re: st: mlogit estimation p-value problem***From:*Andreas Chouliaras <adhoul@gmail.com>

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