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Wiesenfarth, Manuel
Hisgen, Carlos Matías
Kneib, Thomas
Cadarso-Suarez, Carmen
Year of Publication: 
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Courant Research Centre: Poverty, Equity and Growth - Discussion Papers 127
We propose a Bayesian nonparametric instrumental variable approach 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 speci cation 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 extremely challenging in the classical framework, making the Bayesian one advantageous. In simulations, we investigate small sample properties and an investigation of the efect of class size on student performance in Israel provides an illustration of the proposed approach which is implemented in an R package bayesIV.
Markov Chain Monte Carlo methods
Simultaneous credible bands
Document Type: 
Working Paper

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