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A penalized likelihood estimation approach to semiparametric sample selection binary response modeling

Marra, G. and Radice, R. ORCID: 0000-0002-6316-3961 (2013). A penalized likelihood estimation approach to semiparametric sample selection binary response modeling. Electronic Journal of Statistics, 7, pp. 1432-1455. doi: 10.1214/13-EJS814


Sample selection models are employed when an outcome of interest is observed for a restricted non-randomly selected sample of the population. We consider the case in which the response is binary and continuous covariates have a nonlinear relationship to the outcome. We introduce two statistical methods for the estimation of two binary regression models involving semiparametric predictors in the presence of non-random sample selection. This is achieved using a multiple-stage procedure, and a newly developed simultaneous equation estimation scheme. Both approaches are based on the penalized likelihood estimation framework. The problems of identification and inference are also discussed. The empirical properties of the proposed approaches are studied through a simulation study. The methods are then illustrated using data from the American National Election Study where the aim is to quantify public support for school integration. If non-random sample selection is neglected then the predicted probability of giving, for instance, a supportive response may be biased, an issue that can be tackled using the proposed tools.

Publication Type: Article
Additional Information: This is an open access article.
Subjects: H Social Sciences > HA Statistics
Q Science > QA Mathematics
Departments: Cass Business School > Actuarial Science & Insurance
Text - Accepted Version
Available under License Creative Commons Attribution.

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