Large Bayesian VARMAs

Joshua Chan, Eric Eisenstat and Gary Koop

Working Paper series from Rimini Centre for Economic Analysis

Abstract: Vector Autoregressive Moving Average (VARMA) models have many theoretical properties which should make them popular among empirical macroeconomists. However, they are rarely used in practice due to over-parametrization concerns, difficulties in ensuring identification and computational challenges. With the growing interest in multivariate time series models of high dimension, these problems with VARMAs become even more acute, accounting for the dominance of VARs in this field. In this paper, we develop a Bayesian approach for inference in VARMAs which surmounts these problems. It jointly ensures identification and parsimony in the context of an efficient Markov chain Monte Carlo (MCMC) algorithm. We use this approach in a macroeconomic application involving up to twelve dependent variables. We find our algorithm to work successfully and provide insights beyond those provided by VARs.

Date: 2014-11
New Economics Papers: this item is included in nep-ets
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Citations: View citations in EconPapers (16)

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http://www.rcea.org/RePEc/pdf/wp40_14.pdf (application/pdf)

Related works:
Journal Article: Large Bayesian VARMAs (2016)
Working Paper: Large Bayesian VARMAs (2015)
Working Paper: Large Bayesian VARMAs (2014)
Working Paper: Large Bayesian VARMAs (2014)
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Persistent link: https://EconPapers.repec.org/RePEc:rim:rimwps:40_14

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