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    Estimation of a semiparametric recursive bivariate probit model with nonparametric mixing

    Marra, G. and Papageorgiou, Georgios and Radice, Rosalba (2013) Estimation of a semiparametric recursive bivariate probit model with nonparametric mixing. Australian & New Zealand Journal of Statistics 55 (3), pp. 321-342. ISSN 1369-1473.

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    Abstract

    We consider an extension of the recursive bivariate probit model for estimating the effect of a binary variable on a binary outcome in the presence of unobserved confounders, nonlinear covariate effects and overdispersion. Specifically, the model consists of a system of two binary outcomes with a binary endogenous regressor which includes smooth functions of covariates, hence allowing for flexible functional dependence of the responses on the continuous regressors, and arbitrary random intercepts to deal with overdispersion arising from correlated observations on clusters or from the omission of non-confounding covariates. We fit the model by maximizing a penalized likelihood using an Expectation-Maximisation algorithm. The issues of automatic multiple smoothing parameter selection and inference are also addressed. The empirical properties of the proposed algorithm are examined in a simulation study. The method is then illustrated using data from a survey on health, aging and wealth.

    Metadata

    Item Type: Article
    Keyword(s) / Subject(s): nonparametric maximum likelihood estimation, penalised regression spline, recursive bivariate probit model, unobserved confounding
    School: School of Business, Economics & Informatics > Economics, Mathematics and Statistics
    Depositing User: Administrator
    Date Deposited: 22 Oct 2013 10:34
    Last Modified: 28 Jun 2020 22:44
    URI: https://eprints.bbk.ac.uk/id/eprint/8587

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