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    Copula regression spline models for binary outcomes

    Radice, Rosalba and Marra, G. and Wojtys, M. (2015) Copula regression spline models for binary outcomes. Statistics and Computing , ISSN 0960-3174.

    BivProbCop RMW 18 May 2015.pdf - Author's Accepted Manuscript

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    We introduce a framework for estimating the effect that a binary treatment has on a binary outcome in the presence of unobserved confounding. The methodology is applied to a case study which uses data from the Medical Expenditure Panel Survey and whose aim is to estimate the effect of private health insurance on health care utilization. Unobserved confounding arises when variables which are associated with both treatment and outcome are not available (in economics this issue is known as endogeneity). Also, treatment and outcome may exhibit a dependence which cannot be modeled using a linear measure of association, and observed confounders may have a non-linear impact on the treatment and outcome variables. The problem of unobserved confounding is addressed using a two-equation structural latent variable framework, where one equation essentially describes a binary outcome as a function of a binary treatment whereas the other equation determines whether the treatment is received. Non-linear dependence between treatment and outcome is dealt using copula functions, whereas covariate-response relationships are flexibly modeled using a spline approach. Related model fitting and inferential procedures are developed, and asymptotic arguments presented.


    Item Type: Article
    Additional Information: The final publication is available at Springer via
    Keyword(s) / Subject(s): Bivariate binary outcomes, Copula, Endogeneity, Penalized regression spline, Simultaneous equation estimation, Unobserved confounding
    School: Birkbeck Faculties and Schools > Faculty of Business and Law > Birkbeck Business School
    Depositing User: Rosalba Radice
    Date Deposited: 27 Jul 2015 14:30
    Last Modified: 02 Aug 2023 17:17


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