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Activity Number: 175 - Bayesian Theory, Foundations, and Nonparametrics
Type: Contributed
Date/Time: Monday, July 30, 2018 : 10:30 AM to 12:20 PM
Sponsor: Section on Bayesian Statistical Science
Abstract #329611 Presentation
Title: Semiparametric Bayes Model for Multidimensional Instrumental Variables
Author(s): Ryo Kato* and Takahiro Hoshino
Companies: Keio University and Keio University
Keywords: Instrumental variable; Mendelian Randomization; Semiparametric Bayes model; Probit stick-breaking process mixture

We develop a new semiparametric Bayes instrumental variables estimation method. We employ the form of the regression function of the reduced-form equation and the disturbances are modelled nonparametrically to achieve better predictive power of the endogenous variables, whereas we use parametric formulation in the structural equation, which is of interest in inference. Our simulation studies show that under small sample size the proposed method obtains more efficient estimates and very precise credible intervals compared with the existing IV methods. The existing methods fail to reject the null hypothesis with higher probability, due to larger variance of the estimators. Moreover, the mean squared error in the proposed method may be less than 1/30 of that in the existing procedures even in the presence of weak instruments. We applied our proposed method to a Mendelian randomization dataset where a large number of instruments are available and semiparametric specification is appropriate.

Authors who are presenting talks have a * after their name.

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