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Regression spline bivariate probit models: A practical approach to testing for exogeneity

Marra, G., Radice, R. ORCID: 0000-0002-6316-3961 and Filippou, P. (2017). Regression spline bivariate probit models: A practical approach to testing for exogeneity. Communications in Statistics - Simulation and Computation, 46(3), pp. 2283-2298. doi: 10.1080/03610918.2015.1041974

Abstract

Bivariate probit models can deal with a problem usually known as endogeneity. This issue is likely to arise in observational studies when confounders are unobserved. We are concerned with testing the hypothesis of exogeneity (or absence of endogeneity) when using regression spline recursive and sample selection bivariate probit models. Likelihood ratio and gradient tests are discussed in this context and their empirical properties investigated and compared with those of the Lagrange multiplier and Wald tests through a Monte Carlo study. The tests are illustrated using two datasets in which the hypothesis of exogeneity needs to be tested.

Publication Type: Article
Additional Information: This is a pre-copyedited, author-produced version of an article accepted for publication in 'Communications in Statistics - Simulation and Computation' following peer review. The version of record Marra, G., Radice, R. and Filippou, P. (2017). Regression spline bivariate probit models: A practical approach to testing for exogeneity. Communications in Statistics - Simulation and Computation, 46(3), pp. 2283-2298 is available online at: https://doi.org/10.1080/03610918.2015.1041974.
Publisher Keywords: Bivariate probit model, Endogeneity, Gradient test, Lagrange multiplier test, Likelihood ratio test, Non-random sample selection, Penalized regression spline, Wald test
Subjects: H Social Sciences
Q Science > QA Mathematics
Departments: Cass Business School > Actuarial Science & Insurance
URI: http://openaccess.city.ac.uk/id/eprint/20946
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