Vector Autoregression (VAR) has been a standard empirical tool used in macroeconomics and finance. In this paper we discuss how to compare alternative VAR models after they are estimated by Bayesian MCMC methods. In particular we apply a robust version of deviance information criterion (RDIC) recently developed in Li, Zeng, and Yu (2014b) to determine the best candidate model. RDIC is a better information criterion than the widely used deviance information criterion (DIC) when latent variables are involved in candidate models. Empirical analysis using US data shows that the optimal model selected by RDIC can be different from that by DIC.
Li gratefully acknowledges the financial support by the Fundamental Research Funds for the Central Universities, and the Research Funds of Renmin University of China. Yu thanks the Singapore Ministry of Education for Academic Research Fund under grant number MOE2011-T2-2-096. We acknowledge the helpful comments from a referee and the participants at a conference in honor of Peter C. B. Phillips held at Southern Methodist University on November 1–3, 2013.
Zeng, T., Li, Y. and Yu, J. (2014), "Deviance Information Criterion for Comparing VAR Models", Essays in Honor of Peter C. B. Phillips (Advances in Econometrics, Vol. 33), Emerald Group Publishing Limited, Leeds, pp. 615-637. https://doi.org/10.1108/S0731-905320140000033017
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