Abstract #300960

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JSM 2003 Abstract #300960
Activity Number: 115
Type: Topic Contributed
Date/Time: Monday, August 4, 2003 : 10:30 AM to 12:20 PM
Sponsor: Section on Bayesian Stat. Sciences
Abstract - #300960
Title: Bayesian Estimator of Vector-Autoregressive Model
Author(s): Shawn Ni*+ and Dongchu Sun
Companies: University of Missouri, Columbia and University of Missouri, Columbia
Address: Department of Economics, Columbia, MO, 65211-0001,
Keywords: Bayesian VAR ; entropy loss ; latent parameters ; MCMC
Abstract:

The present study concerns Bayesian estimation of the vector-autoregression (VAR) model. We show that under the normality assumption a part of the entropy loss coincides with the parametric loss function proposed by Zellner for estimation of simultaneous equations model. Based on the entropy loss, the Bayesian estimator is different from the posterior mean and involves frequentist moments of VAR variables. We find that the condition that allows for a closed-form expression of the frequentist expectation is violated even when the VAR is stationary, making it difficult to compute the Bayesian estimates via standard Markov chain Monte Carlo (MCMC) procedures. We propose an MCMC simulation of the Bayesian estimator without using the closed-form expression of the frequentist expectation. A novelty of our MCMC algorithms is that they jointly simulate the posteriors of frequentist moments of VAR variables as well as the posteriors of VAR parameters. Numerical simulations show that the algorithms are surprisingly efficient.

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