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Estimation of ordinal response models, accounting for sample selection bias


  • Alfonso Miranda

    (Keele University)


Studying behaviour in economics, sociology, and statistics often involves fitting a model in which the outcome is an ordinal response which is only observed for a subsample of subjects. (For example, questions about health satisfaction in a survey might be asked only of respondents who have a particular health condition.) In this situation, estimation of the ordinal response model without taking account of this "sample selection" effect, using e.g. -ologit- or -oprobit-, may lead to biased parameter estimates. (In the earlier example, unobserved factors that increase the chances of having the health condition may be correlated with the unobserved factors that affect health satisfaction.) The program -gllamm- can be used to estimate ordinal response models accounting for sample selection, by ML. This paper describes a "wrapper" program, -osm-, that calls -gllamm- to fit the model. It accepts data in a simple structure, has a straightforward syntax and, moreover, reports output in a manner that is easily interpretable. One important feature of -osm- is that the log-likelihood can be evaluated using adaptive quadrature.

Suggested Citation

  • Alfonso Miranda, 2005. "Estimation of ordinal response models, accounting for sample selection bias," United Kingdom Stata Users' Group Meetings 2005 11, Stata Users Group.
  • Handle: RePEc:boc:usug05:11

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    Cited by:

    1. Alfonso Miranda & Sophia Rabe-Hesketh, 2006. "Maximum likelihood estimation of endogenous switching and sample selection models for binary, ordinal, and count variables," Stata Journal, StataCorp LP, vol. 6(3), pages 285-308, September.

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