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From |
John Litfiba <cariboupad@gmx.fr> |

To |
statalist@hsphsun2.harvard.edu |

Subject |
Re: st: too good to be true : lr test in mlogit? |

Date |
Sat, 14 May 2011 11:31:58 +0200 |

Dear Marten and Joerg, Thank you for this idea : it can be a interesting solution indeed! Maybe I shall add 2 remarks here : 1) The log likelihood doesnt converge when I try to fit a random or fixed effect with xtlogit on my entire dataset.. I have to chose a very "small" (well, compared to the total size of the sample) of about 10000 observations in order to see the results... otherwise I get an error message after 3 or 4 iterations 2) The idea of running lets say M regressions over randomly chose samples could be a solution, but it is statistically valid ? I mean if I obtain the distribution of the parameters across my M simulation can I infer something on the parameters of the simulation that should have been done on the entire dataset ? Have a good day Best Regards, On 13 May 2011 15:11, Joerg Luedicke <joerg.luedicke@gmail.com> wrote: > On Fri, May 13, 2011 at 6:56 AM, Maarten Buis <maartenlbuis@gmail.com> wrote: >> On Fri, May 13, 2011 at 11:21 AM, John Litfiba wrote: >>> But after various tentatives I think that it is not computationally >>> feasible (at least on my PC) to fit a random or fixed effect logit on >>> such a large dataset >> >> I would not give up on that yet. I would select my model on a sample >> of your data (I would sample higher level units, rather than >> individual observations), > > This is what I would do, too. In addition, I would be interested in > the variation of results from different samples. I would recommend > setting up a program that is drawing a random sample, say 1%, running > the model, and saving the results. Then let that program run a 1000 or > whatever times. (How this can be done is described here: > http://www.stata.com/statalist/archive/2011-04/msg01220.html) Small > variation in results could then be indicative of the fact that it is > pointless anyway to have a sample size of > 2mio. In that case, i.e. > low variation of results, you can also compare your xtlogit model with > the model you have now etc. Large variation could be an indication of > the fact that a very large sample size may be appropriate. You could > also play with different sample sizes (say, 1%, 3%, 5%) and see how > the variation of results changes across sample size. At the very > least, this will provide useful information about your sample and > might be helpful with respect to the specification of your model. > > J. > * > * 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/ > * * 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: too good to be true : lr test in mlogit?***From:*Maarten Buis <maartenlbuis@gmail.com>

**References**:**st: too good to be true : lr test in mlogit?***From:*John Litfiba <cariboupad@gmx.fr>

**Re: st: too good to be true : lr test in mlogit?***From:*Maarten Buis <maartenlbuis@gmail.com>

**Re: st: too good to be true : lr test in mlogit?***From:*John Litfiba <cariboupad@gmx.fr>

**Re: st: too good to be true : lr test in mlogit?***From:*Maarten Buis <maartenlbuis@gmail.com>

**Re: st: too good to be true : lr test in mlogit?***From:*Joerg Luedicke <joerg.luedicke@gmail.com>

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