首页 | 本学科首页   官方微博 | 高级检索  
     检索      


Bayesian Nonparametric Instrumental Variables Regression Based on Penalized Splines and Dirichlet Process Mixtures
Authors:Manuel Wiesenfarth  Carlos Matías Hisgen  Thomas Kneib  Carmen Cadarso-Suarez
Institution:1. University of Mannheim, Department of Economics 68131, Mannheim, Germany (mwiesen@uni-mannheim.de);2. Department of Economics, Universidad Nacional del Nordeste, Argentina (mhisgen@gmail.com);3. Department of Statistics and Econometrics, Georg-August-Universit?t G?ttingen, 37073 Gottingen, Germany (tkneib@uni-goettingen.de);4. Department of Biostatistics, School of Medicine, University of Santiago de Compostela, 15782 Santiago de Compostela, Spain (carmen.cadarso@usc.es)
Abstract:We propose a Bayesian nonparametric instrumental variable approach under additive separability that allows us to correct for endogeneity bias in regression models where the covariate effects enter with unknown functional form. Bias correction relies on a simultaneous equations specification with flexible modeling of the joint error distribution implemented via a Dirichlet process mixture prior. Both the structural and instrumental variable equation are specified in terms of additive predictors comprising penalized splines for nonlinear effects of continuous covariates. Inference is fully Bayesian, employing efficient Markov chain Monte Carlo simulation techniques. The resulting posterior samples do not only provide us with point estimates, but allow us to construct simultaneous credible bands for the nonparametric effects, including data-driven smoothing parameter selection. In addition, improved robustness properties are achieved due to the flexible error distribution specification. Both these features are challenging in the classical framework, making the Bayesian one advantageous. In simulations, we investigate small sample properties and an investigation of the effect of class size on student performance in Israel provides an illustration of the proposed approach which is implemented in an R package bayesIV. Supplementary materials for this article are available online.
Keywords:Endogeneity  Markov chain Monte Carlo methods  Simultaneous credible bands
设为首页 | 免责声明 | 关于勤云 | 加入收藏

Copyright©北京勤云科技发展有限公司  京ICP备09084417号