Noninformative Priors and Frequentist Risks of Bayesian Estimators of Vector-Autoregressive Models

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Noninformative Priors and Frequentist Risks of Bayesian Estimators of Vector-Autoregressive Models

Please use this identifier to cite or link to this item: http://hdl.handle.net/10355/2729

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Title: Noninformative Priors and Frequentist Risks of Bayesian Estimators of Vector-Autoregressive Models
Author: Ni, Shawn, 1962-; Dongchu, Sun
Keywords: Bayes estimator
VAR
covariance matrix
Date: 2002
Publisher: Department of Economics
Citation: Department of Economics, 2002
Series/Report no.: Working papers (Department of Economics);WP 02-10
Abstract: In this study, we examine posterior properties and frequentist risks of Bayesian estimators based on several non-informative priors in Vector Autoregressive (VAR) models. We prove existence of the posterior distributions and posterior moments under a general class of priors. Using a variety of priors in this class we conduct numerical simulations of posteriors. We find that in most examples Bayesian estimators with a shrinkage prior on the VAR coefficients and the reference prior of Yang and Berger (1994) on the VAR covariance matrix dominate MLE, Bayesian estimators with the diffuse prior, and Bayesian estimators with the prior used in RATS. We also examine the informative Minnesota prior and find that its performance depends on the nature of the data sample and on the tightness of the Minnesota prior. A tightly set Minnesota prior is better when the data generating processes are similar to random walks, but the shrinkage prior or constant prior can be better otherwise.
URI: http://hdl.handle.net/10355/2729

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  • Economics publications (MU) [120]
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