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

被引：6

作者：

Marra, Giampiero ^{[1
]}

Radice, Rosalba ^{[2
]}

Filippou, Panagiota ^{[1
]}

机构：

[1] UCL, Dept Stat Sci, Gower St, London WC1E 6BT, England

[2] Birkbeck Univ London, Dept Econ Math & Stat, London, England

来源：

COMMUNICATIONS IN STATISTICS-SIMULATION AND COMPUTATION | 2017年 / 46卷 / 03期

基金：

英国工程与自然科学研究理事会;

关键词：

Bivariate probit model; Endogeneity; Gradient test; Lagrange multiplier test; Likelihood ratio test; Non-random sample selection; Penalized regression spline; Wald test; HEALTH-INSURANCE; CARE UTILIZATION;

D O I：

10.1080/03610918.2015.1041974

中图分类号：

O21 [概率论与数理统计]; C8 [统计学];

学科分类号：

020208 ; 070103 ; 0714 ;

摘要：

Bivariate probit models can dea lwith 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.

引用

页码：2283 / 2298

页数：16

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