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

References

• Banasik, J., Crook, J. (2007). Reject inference, augmentation, and sample selection. European Journal of Operational Research 183:1582–1594.
   Google Scholar
• Bärnighausen, T., Bor, J., Wandira-Kazibwe, S., Canning, D. (2011). Correcting HIV prevalence estimates for survey nonparticipation using Heckman-type selection models. Epidemiology 22:27–35.
   Google Scholar
• Buchmueller, T. C., Grumbach, K., Kronick, R., Kahn, J. G. (2005). Book review: The effect of health insurance on medical care utilization and implications for insurance expansion: A review of the literature. Medical Care Research and Review 62:3–30.
   Google Scholar
• Chib, S., Greenberg, E. (2007). Semiparametric modeling and estimation of instrumental variable models. Journal of Computational and Graphical Statistics 16:86–114.
   Google Scholar
• Cuddeback, G., Wilson, E., Orme, J., Combs-Orme, T. (2004). Detecting and statistically correcting sample selection bias. Journal of Social Service Research 30:19–33.
   Google Scholar
• de Ven, W. V., Praag, B. V. (1981). The demand for deductibles in private health insurance: a probit model with sample selection. Journal of Econometrics 17:229–252.
   Google Scholar
• Eilers, P. H. C., Marx, B. D. (1996). Flexible smoothing with B-splines and penalties. Statistical Science 11(2):89–121.
   Google Scholar
• Goldman, D., Bhattacharya, J., McCaffrey, D., Duan, N., Leibowitz, A., Joyce, G., Morton, S. (2001). Effect of insurance on mortality in an HIV-positive population in care. Journal of the American Statistical Association 96:883–894.
   Google Scholar
• Greene, W. H. (2012). Econometric Analysis. New York: Prentice Hall.
   Google Scholar
• Han, S., Vytlacil, E. J. (2014). Identification in a generalization of bivariate probit models with endogenous regressors. Available at: http://econ.sites.olt.ubc.ca/files/2013/12/pdf_paper_seminar_sukjin_han.pdf.
   Google Scholar
• Hastie, T., Tibshirani, R. (1993). Varying-coefficient models. Journal of the Royal Statistical Society Series B 55:757–796.
   Google Scholar
• Heckman, J. (1978). Dummy endogenous variables in a simultaneous equation system. Econometrica 46:931–959.
   Google Scholar
• Heckman, J. (1979). Sample selection bias as a specification error. Econometrica 47:153–161.
   Google Scholar
• Holly, A., Gardiol, L., Domenighetti, G., Brigitte, B. (1998). An econometric model of health care utilization and health insurance in switzerland. European Economic Review 42(3-5):513–522.
   Google Scholar
• Kauermann, G. (2005). Penalized spline smoothing in multivariable survival models with varying coefficients. Computational Statistics and Data Analysis 49:169–186.
   Google Scholar
• Kawatkar, A. A., Nichol, M. B. (2009). Estimation of causal effects of physical activity on obesity by a recursive bivariate probit model. Value in Health 12:A131–A132.
   Google Scholar
• Latif, E. (2009). The impact of diabetes on employment in Canada. Health Economics 18:577–589.
   Google Scholar
• Liu, H., Tu, W. (2012). A semiparametric regression model for paired longitudinal outcomes with application in childhood blood pressure development. Annals of Applied Statistics 6:1861–1882.
   Google Scholar
• Maddala, G. S. (1983). Limited Dependent and Qualitative Variables in Econometrics. Cambridge: Cambridge University Press.
   Google Scholar
• Marra, G., Radice, R. (2010). Penalised regression splines: Theory and application to medical research. Statistical Methods in Medical Research 19:107–125.
   Google Scholar
• Marra, G., Radice, R. (2011a). Estimation of a semiparametric recursive bivariate probit model in the presence of endogeneity. Canadian Journal of Statistics 39:259–279.
   Google Scholar
• Marra, G., Radice, R. (2011b). A flexible instrumental variable approach. Statistical Modelling 11:581–603.
   Google Scholar
• Marra, G., Radice, R. (2013). A penalized likelihood estimation approach to semiparametric sample selection binary response modeling. Electronic Journal of Statistics 7:1432–1455.
   Google Scholar
• Marra, G., Radice, R. (2015). SemiParBIVProbit: Semiparametric Bivariate Probit Modelling. R package version 3.2–13.2.
   Google Scholar
• Marra, G., Radice, R., Missiroli, S. (2014). Testing the hypothesis of absence of unobserved confounding in semiparametric bivariate probit models. Computational Statistics 29:715–741.
   Google Scholar
• Monfardini, C., Radice, R. (2008). Testing exogeneity in the bivariate probit model: A Monte Carlo study. Oxford Bulletin of Economics and Statistics 70:271–282.
   Google Scholar
• Montmarquette, C., Mahseredjiana, S., Houle, R. (2001). The determinants of university dropouts: a bivariate probability model with sample selection. Economics of Education Review 20:475–484.
   Google Scholar
• Nummi, T., Pan, J., Siren, T., Liu, K. (2011). Testing for cubic smoothing splines under dependent data. Biometrics 67:871–875.
   Google Scholar
• Perez, F., Zvandaziva, C., Engelsmann, B., Dabis, F. (2006). Acceptability of routine hiv testing (opt-out) in antenatal services in two rural districts of zimbabwe. Journal of Acquired Immune Deficiency Syndromes 41:514–520.
   Google Scholar
• R Development Core Team, (2015). R: A Language and Environment for Statistical Computing. Vienna, Austria: R Foundation for Statistical Computing, ISBN 3-900051-07-0.
   Google Scholar
• Radice R., Marra G., Wojtys, M. (2016). Copula Regression Spline Models for Binary Outcomes. Statistics and Computing 26(5):981–995.
   Google Scholar
• Radice, R., Marra, G., Zanin, L. (2013). On the effect of obesity on employment in the presence of observed and unobserved confounding. Statistica Neerlandica 67:436–455.
   Google Scholar
• Ruppert, D., Wand, M., Carroll, R. (2003). Semiparametric Regression. New York: Cambridge University Press.
   Google Scholar
• Terrell, G. (2002). The gradient statistic. Computing Science and Statistics 34:206–215.
   Google Scholar
• Vargas, T., Ferrari, S., Lemonte, A. (2013). Gradient statistic: Higher-order asymptotics and bartlett-type correction. World Bank Economic Review 7:43–61.
   Google Scholar
• Wilde, J. (2000). Identification of multiple equation probit models with endogenous dummy regressors. Economics Letters 69:309–312.
   Google Scholar
• Wood, S. N. (2006). Generalized Additive Models: An Introduction With R. London: Chapman & Hall.
   Google Scholar