Regression spline bivariate probit models: A practical approach to testing for exogeneity. Issue 3 (16th March 2017)

• Record Type: Journal Article
• Authors: Marra, Giampiero; Radice, Rosalba; Filippou, Panagiota
• Is Part Of: Communications in statistics. Volume 46:Issue 3(2017)
• Page Start: 2283
• Page End: 2298
• Publication Date: 2017-03-16
• Subjects: Bivariate probit model -- Endogeneity -- Gradient test -- Lagrange multiplier test -- Likelihood ratio test -- Non-random sample selection -- Penalized regression spline -- Wald test; 62G08 -- 62G10; Mathematical statistics -- Periodicals; Mathematical statistics -- Data processing -- Periodicals; Digital computer simulation -- Periodicals; 519.5
• DOI: 10.1080/03610918.2015.1041974
• Languages: English
• ISSNs: 0361-0918

Abstract

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.