Maximum Likelihood Estimation of Endogenous Switching And Sample Selection Models for Binary, Count, And Ordinal Variables

Alfonso Miranda and Sophia Rabe-Hesketh

No KERP 2005/14, Keele Economics Research Papers from Centre for Economic Research, Keele University

Abstract: Studying behaviour in economics, sociology, and statistics often involves fitting models in which the response variable depends on a dummy variable (also known as a regime switch variable) or in which the response variable is observed only if a particular selection condition is met. In either case, standard regression techniques deliver inconsistent estimators if unobserved factors that affect the response are correlated with unobserved factors that affect the switching or selection variable. Consistent estimators can be obtained by maximum likelihood estimation of a joint model of the outcome and switching or selection variable. This paper describes a ‘wrapper’ program, ssm, that calls gllamm (Rabe-Hesketh et al. 2004a) to fit such models. The wrapper accepts data in a simple structure, has a straightforward syntax, and reports output in a manner that is easily interpretable. One important feature of ssm is that the log-likelihood can be evaluated using adaptive quadrature (Rabe- Hesketh and Skrondal 2002; Rabe-Hesketh et al. 2005)

Keywords: Endogenous switching; sample selection; binary variable; count data; ordinal variable; probit; poisson regression; adaptive quadrature; gllamm; wrapper; ssm. (search for similar items in EconPapers)
JEL-codes: C13 C31 C35 C87 I12 (search for similar items in EconPapers)
Pages: 34 pages
Date: 2005-12
New Economics Papers: this item is included in nep-dcm and nep-ecm
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Published in the STATA Journal, 6(3), 2006, pages 285-308. http://www.stata-journal.com/abstracts/st0107.pdf

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