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Working Paper
Testing for Endogeneity: A Moment-Based Bayesian Approach
A standard assumption in the Bayesian estimation of linear regression models is that the regressors are exogenous in the sense that they are uncorrelated with the model error term. In practice, however, this assumption can be invalid. In this paper, under the rubric of the exponentially tilted empirical likelihood, we develop a Bayes factor test for endogeneity that compares a base model that is correctly specified under exogeneity but misspecified under endogeneity against an extended model that is correctly specified in either case. We provide a comprehensive study of the log-marginal ...
Working Paper
Bayesian Estimation and Comparison of Conditional Moment Models
We provide a Bayesian analysis of models in which the unknown distribution of the outcomes is speci?ed up to a set of conditional moment restrictions. This analysis is based on the nonparametric exponentially tilted empirical likelihood (ETEL) function, which is constructed to satisfy a sequence of unconditional moments, obtained from the conditional moments by an increasing (in sample size) vector of approximating functions (such as tensor splines based on the splines of each conditioning variable). The posterior distribution is shown to satisfy the Bernstein-von Mises theorem, subject to a ...
Working Paper
DSGE-SVt: An Econometric Toolkit for High-Dimensional DSGE Models with SV and t Errors
Currently, there is growing interest in dynamic stochastic general equilibrium (DSGE) models that have more parameters, endogenous variables, exogenous shocks, and observables than the Smets and Wouters (2007) model, and substantial additional complexities from non-Gaussian distributions and the incorporation of time-varying volatility. The popular DYNARE software package, which has proved useful for small and medium-scale models is, however, not capable of handling such models, thus inhibiting the formulation and estimation of more re-alistic DSGE models. A primary goal of this paper is to ...