[Date Prev][Date Next][Thread Prev][Thread Next][Date index][Thread index]

From |
jc1926@gmx.de |

To |
statalist@hsphsun2.harvard.edu |

Subject |
Re: st: RE: RE: out-of-sample predictions with GLM |

Date |
Thu, 26 Jul 2007 16:09:19 +0200 |

Hello, Many thanks for the follow-up. I do have a situation where the proportions should add up to one, as in the city budget example. In my case, my unit of observation is the share of a new car model as a percent of the total market for new cars in a given year. I have this share over several years. Because there are hundreds of car models in my data, I don't think it would be feasible to use a multinomial logit. None of my observation are zero or one. The x's are mostly car attributes for the particular car models, many of which change over time. I pool all the data and estimate with: glm share_i_year_t x_i_t, fam(binomial) link(logit) robust Company dummies are included as fixed effects. The predicted shares from the model sum to 1 for a given year, which is gratifying. Out-of-sample predictions do not sum to one, which is not gratifying but also not unexpected. Maarten's comment makes me wonder if there is a better approach. Any further insights would be welcome. -------- Original-Nachricht -------- Datum: Thu, 26 Jul 2007 15:17:43 +0200 Von: "Maarten Buis" <M.Buis@fsw.vu.nl> An: statalist@hsphsun2.harvard.edu Betreff: st: RE: RE: out-of-sample predictions with GLM > ---I wrote: > > A situation where you want proportions to add up to 1 > > within an observation occurs when you have for each > > observation multiple proportions, e.g. for each city > > you have the proportions of the city budget spent on > > safety, education, transport, etc. In this case the > > proportions within a city should add up to 1. A model > > that deals with this situation is -dirifit-,see: > > http://home.fsw.vu.nl/m.buis/software/dirifit.html > > A couple of months ago I posted a fractional multinomial > logit -fmlogit- on the statalist > http://www.stata.com/statalist/archive/2007-05/msg00449.html > > -fmlogit- relates to -dirifit- like fractional logit relates > to beta regression. > > Can you tell us a bit more about your problem? What are > the dependent and independent variables? Do you have many > observations with a proportion equal to 0 or 1. Papke and > Wooldridge are overly optimistic about the ability of > dealing with those problems using the fractional > (binomial or multinomial) logit model. > > Hope this helps, > Maarten > > ----------------------------------------- > Maarten L. Buis > Department of Social Research Methodology > Vrije Universiteit Amsterdam > Boelelaan 1081 > 1081 HV Amsterdam > The Netherlands > > visiting address: > Buitenveldertselaan 3 (Metropolitan), room Z434 > > +31 20 5986715 > > http://home.fsw.vu.nl/m.buis/ > ----------------------------------------- > > > * > * 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/ -- Psssst! Schon vom neuen GMX MultiMessenger gehört? Der kanns mit allen: http://www.gmx.net/de/go/multimessenger * * 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**:**Re: st: RE: RE: out-of-sample predictions with GLM***From:*Maarten buis <maartenbuis@yahoo.co.uk>

**References**:**st: RE: RE: out-of-sample predictions with GLM***From:*"Maarten Buis" <M.Buis@fsw.vu.nl>

- Prev by Date:
**st: Stata10 SE and do files editor** - Next by Date:
**Re: st: RE: RE: out-of-sample predictions with GLM** - Previous by thread:
**st: RE: RE: out-of-sample predictions with GLM** - Next by thread:
**Re: st: RE: RE: out-of-sample predictions with GLM** - Index(es):

© Copyright 1996–2016 StataCorp LP | Terms of use | Privacy | Contact us | What's new | Site index |