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From |
Daniel Simon <dhs29@cornell.edu> |

To |
statalist@hsphsun2.harvard.edu |

Subject |
Re: st: model for fractional data with panel data |

Date |
Wed, 07 Nov 2007 13:37:47 -0500 |

Austin - just a quick follow-up question. In my case (I was not the original poster on this thread), I do have a non-trivial number of cases where y=0. Therefore, the glm approach with fixed effects seems appropriate - yes? thanks. Daniel

At 11:56 AM 11/7/2007 -0500, Austin Nichols wrote:

Arne-- I'm not sure the concern about incidental parameters applies here. To my mind, the question is, is there anything to be gained by using -glm- with indicator variables to capture fixed effects to estimate instead of transforming y by generating a new variable lny=ln(y) or logity=logit(y) or invlogity=invlogit(y) and I'm not sure there is, in this case. The poster specified that y measured proportions strictly between 0 and 1, i.e. on the open interval. That is the crucial point--there are no obs with y=0 or y=1. In this case, you may be better off with -xtreg- (or -xtivreg2- with more SE adjustments) than -glm- if only because estimation is so much faster! But you will get numerically different answers, of course... since y=f(Xb+e) is not the same as y=f(Xb)+e webuse psidextract, clear tsset id t gen w=wks/53 g ilw=invlogit(w) qui su ilw replace ilw=ilw/r(sd) qui reg ilw lw uni south smsa, cluster(id) est sto reg qui glm w lw uni south smsa, link(logit) fam(gauss) cl(id) est sto glm qui xtreg ilw lw uni south smsa, cluster(id) fe est sto xtreg qui xi: glm w lw uni sou sms i.id, link(logit) fam(gauss) cl(id) est sto xtglm esttab *, keep(lwage union south smsa) mti ---------------------------------------------------------------------------- (1) (2) (3) (4) reg glm xtreg xtglm ---------------------------------------------------------------------------- main lwage 0.139* 0.127* 0.0598 0.162 (2.41) (2.03) (0.83) (1.55) union -0.309*** -0.286*** 0.158 0.171 (-6.09) (-6.22) (1.33) (1.38) south 0.0361 0.0404 -0.122 -0.275 (0.67) (0.76) (-0.66) (-1.16) smsa 0.0242 0.0176 0.0304 0.0468 (0.45) (0.35) (0.35) (0.38) ---------------------------------------------------------------------------- N 4165 4165 4165 4165 ---------------------------------------------------------------------------- t statistics in parentheses * p<0.05, ** p<0.01, *** p<0.001 Having accepted you might transform y, the question then is which transformation is appropriate, and for that you need some theory. Neglecting theory, you might explore whether regressions using lny=ln(y) or logity=logit(y) or invlogity=invlogit(y) as the depvar produce predictions that make more sense and residuals that look less correlated with your transformed depvar. tw function y=50*invlogit(x)-31||function y=logit(x)||function y=ln(x) On 11/7/07, Arne Risa Hole <arnehole@gmail.com> wrote: > There was an extremely useful discussion on the list recently about > this issue in the context of fixed effects binary logit models. In > short, adding the fixed effects 'by hand' results in biased estimates > unless the number of time periods is large. See the thread starting > with: > > http://www.stata.com/statalist/archive/2007-10/msg00935.html > > Arne > * * 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/

* * 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: model for fractional data with panel data***From:*"Austin Nichols" <austinnichols@gmail.com>

**References**:**Re: st: model for fractional data with panel data***From:*"Arne Risa Hole" <arnehole@gmail.com>

**st: model for fractional data with panel data***From:*Ilaria Tucci <ilale78@yahoo.it>

**Re: st: model for fractional data with panel data***From:*Daniel Simon <dhs29@cornell.edu>

**Re: st: model for fractional data with panel data***From:*"Austin Nichols" <austinnichols@gmail.com>

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