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From |
Joseph Coveney <jcoveney@bigplanet.com> |

To |
Statalist <statalist@hsphsun2.harvard.edu> |

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

Date |
Fri, 05 Sep 2003 17:06:07 +0900 |

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/

**Follow-Ups**:**Re: st: Fixed effects ordinal probit regression***From:*Ulrich Kohler <kohler@wz-berlin.de>

**Re: st: Fixed effects ordinal probit regression***From:*Mark Schaffer <M.E.Schaffer@hw.ac.uk>

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