Semiparametric Bayes Instrumental Variable Estimation with Many Weak Instruments

Ryo Kato and Takahiro Hoshino
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Takahiro Hoshino: Department of Economics, Keio University, Japan and RIKEN Center for Advanced Intelligence Project, Japan

No DP2018-14, Discussion Paper Series from Research Institute for Economics & Business Administration, Kobe University

Abstract: 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 preditive 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 e￠ cient estimates and very precise credible intervals compared with 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. This is a weak instrument case; hence, the non-Bayesian IV approach yields inefficient estimates. We obtained statistically significant results that cannot be obtained by the existing methods, including standard Bayesian IV.

Keywords: Instrumental variable; Mendelian Randomization; Semiparametric Bayes model; Probit stick-breaking process mixture (search for similar items in EconPapers)
Pages: 26 pages
Date: 2018-05
New Economics Papers: this item is included in nep-ecm
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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