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Estimation of a semiparametric recursive bivariate probit model in the presence of endogeneity

Marra, G; Radice, R; (2011) Estimation of a semiparametric recursive bivariate probit model in the presence of endogeneity. Canadian Journal of Statistics , 39 , Article 2. 10.1002/cjs.10100.

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Abstract

Abstract The classic recursive bivariate probit model is of particular interest to researchers since it allows for the estimation of the treatment effect that a binary endogenous variable has on a binary outcome in the presence of unobservables. In this article, the authors consider the semiparametric version of this model and introduce a model fitting procedure which permits to estimate reliably the parameters of a system of two binary outcomes with a binary endogenous regressor and smooth functions of continuous covariates. They illustrate the empirical validity of the proposal through an extensive simulation study. The approach is applied to data from a survey, conducted in Botswana, on the impact of education on women’s fertility. Some studies suggest that the estimated effect could have been biased by the possible endogeneity arising because unobservable confounders (e.g., ability and motivation) are associated with both fertility and education. The Canadian Journal of Statistics 39: 259–279; 2011 © 2011 Statistical Society of Canada

Type:Article
Title:Estimation of a semiparametric recursive bivariate probit model in the presence of endogeneity
DOI:10.1002/cjs.10100
Publisher version:http://dx.doi.org/10.1002/cjs.10100
Additional information:keywords: Average treatment effect, binary data, identification by functional form, penalized regression spline, recursive bivariate probit model, simultaneous equation estimation keywords: Average treatment effect, binary data, identification by functional form, penalized regression spline, recursive bivariate probit model, simultaneous equation estimation
UCL classification:UCL > School of BEAMS > Faculty of Maths and Physical Sciences > Statistical Science

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