Bayesian Model Averaging for Model Implied Instrumental Variable Two Stage Least Squares Estimators

Abstract

Model-Implied Instrumental Variable Two-Stage Least Squares (MIIV-2SLS) is a limited information, equation-by-equation, non-iterative estimator for latent variable models. Associated with this estimator are equation specific tests of model misspecification. We propose an extension to the existing MIIV-2SLS estimator that utilizes Bayesian model averaging which we term Model-Implied Instrumental Variable Two-Stage Bayesian Model Averaging (MIIV-2SBMA). MIIV-2SBMA accounts for uncertainty in optimal instrument set selection, and provides powerful instrument specific tests of model misspecification and instrument strength. We evaluate the performance of MIIV-2SBMA against MIIV-2SLS in a simulation study and show that it has comparable performance in terms of parameter estimation. Additionally, our instrument specific overidentification tests developed within the MIIV-2SBMA framework show increased power to detect model misspecification over the traditional equation level tests of model misspecification. Finally, we demonstrate the use of MIIV-2SBMA using an empirical example.Comment: 31 pages, 8 figures, supplementary materials available upon reques