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From |
"Nick Cox" <n.j.cox@durham.ac.uk> |

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

Subject |
st: RE: RE: Dependent continuous variable with bounded range |

Date |
Tue, 15 Apr 2008 17:34:52 +0100 |

Thanks to Jay for mentioning -betafit- (from SSC). -betafit- does not purport to support panel structure. Off-hand, I can't say how much of a problem that might be. If this were my problem, I'd be looking at -xtgee-. Nick n.j.cox@durham.ac.uk Verkuilen, Jay Nick Cox outlined several strategies, to which I will add just a bit: (1) How close to the boundaries are your observations? If the distribution looks reasonably symmetric, you probably won't gain much from using a specialized model that "knows" about the boundaries. If you have some skew due to the ceiling or floor but not a truly L- or J-shaped distribution, the logit transformation will probably normalize your errors enough to do ordinary panel regression models. If the distribution is truly L- or J-shaped, no transformation will fix things up. (2) -betafit- (by Nick, Maarten, et al) with clustered robust SEs is a viable alternative that uses the linking strategy. This uses the beta distribution as an error model. It won't adjust for the autoregressive nature of panel data, though, but maybe that'll work well enough. (3) If you want to use the GLM approach and are willing to move to different software (SAS or winBUGS), I can give you examples to do random effects and AR-adjusted beta regression. (One of these days, I want to port a random effects and GEE betareg over to Stata; no time right now.) * * 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**:**st: Dependent continuous variable with bounded range***From:*"Pavlos C. Symeou" <p.symeou@jbs.cam.ac.uk>

**st: RE: Dependent continuous variable with bounded range***From:*"Verkuilen, Jay" <JVerkuilen@gc.cuny.edu>

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