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

From |
Ulrich Kohler <kohler@wz-berlin.de> |

To |
statalist@hsphsun2.harvard.edu |

Subject |
Re: st: Fixed effects ordinal probit regression |

Date |
Fri, 5 Sep 2003 10:50:48 +0200 |

I don't think it is that easy. Adding individual dummy-variables will increase the number of coefficients with the number of observations. I am not a statistician but I somehow remember to read that this violates some basic assumption of Likelihood estimation---and that this is the reason why we use the conditional logit model for the binary fixed-effects model. Uli Joseph Coveney wrote: > James Shaw wrote: > > I was wondering if there is such a thing as fixed effects ordinal probit > > regression. If so, could one simply add dummy variables for the panel > > indicator (e.g., subject id) to the ordinal probit model to obtain fixed > > effects estimates? Also, when estimating a fixed effects regression > > model with a subject-level effect, how problematic is it if there are > > missing observations on the dependent variable for some subjects (i.e., > > unbalanced panels)? > > --------------------------------------------------------------------------- >- > > By analogy to -areg , absorb()-, it seems feasible to create dummy > (indicator) variables for the panel identifier with -oprobit-, but doing > this in an ordered categorical regression risks having "note: [X] > observations completely determined. Standard errors questionable." at the > bottom of the -oprobit- output. This would raise suspicions about Wald > tests, although in a test case that I tried out where this happens (see > do-file below), the Wald test agrees well with the corresponding likelihood > ratio test. If panels are dropped due to collinearity in fitting the full > model, then likelihood-ratio testing with the reduced (nested) models is > problematic unless the same panels are fortuitously dropped in the latter. > > When observations are missing, handling the panel as a fixed effect seems > to be mechanically possible--in the test case, -oprobit- attained > convergence and didn't seem to drop any panels with a missing value--but it > might be worthwhile to perform Monte Carlo simulations in order to > determine whether hypothesis testing and parameter estimates behave as > expected in such a circumstance before using -oprobit- on an unknown > dataset with missing observations. > > -oprobit , cluster()- might serve as an alternative in some circumstances. > With enough panels, another alternative would be to consider the panel as a > random effect, and use -reoprob- or -gllamm-. > > Joseph Coveney > > > --------------------------------------------------------------------------- >- > > clear > set more off > set seed 20030906 > set obs 6 > forvalues i = 1/6 { > generate float var`i' = 0.7 > quietly replace var`i' = 1.0 in `i' > } > mkmat var*, matrix(A) > local means m1 > forvalues i = 2/6 { > local means = "`means'" + " m`i'" > } > drawnorm `means', n(40) corr(A) clear > generate byte pid = _n > forvalues i = 1/6 { > generate byte res`i' = 1 + int(norm(m`i') / 0.2) > } > matrix drop A > drop m* > reshape long res, i(pid) j(tim) > xi: oprobit res i.tim i.pid, nolog > estimates store A > test _Itim_2 _Itim_3 _Itim_4 _Itim_5 _Itim_6 > xi: oprobit res i.pid, nolog > lrtest A, stats > xi: oprobit res i.tim, cluster(pid) nolog > xi: reoprob res i.tim, i(pid) quad(30) nolog > // consider -quadchk- here > drop if uniform() > 0.85 > xi: oprobit res i.tim i.pid, nolog > estimates store A > test _Itim_2 _Itim_3 _Itim_4 _Itim_5 _Itim_6 > xi: oprobit res i.pid, nolog > lrtest A, stats > xi: oprobit res i.tim, cluster(pid) nolog > xi: reoprob res i.tim, i(pid) quad(30) nolog > exit > > --------------------------------------------------------------------------- >- > > > > > * > * 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/ -- kohler@wz-berlin.de * * 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/

**References**:**Re: st: Fixed effects ordinal probit regression***From:*Joseph Coveney <jcoveney@bigplanet.com>

- Prev by Date:
**Re: st: Fixed effects ordinal probit regression** - Next by Date:
**st: loading a big dataset in Stata** - Previous by thread:
**Re: st: Fixed effects ordinal probit regression** - Next by thread:
**Re: st: Fixed effects ordinal probit regression** - Index(es):

© Copyright 1996–2021 StataCorp LLC | Terms of use | Privacy | Contact us | What's new | Site index |