Large Bayesian VARMAs

Chan, Joshua C.C. and Eisenstat, Eric and Koop, Gary (2014) Large Bayesian VARMAs. Discussion paper. University of Strathclyde, Glasgow.

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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-parameterization 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.

ORCID iDs

Chan, Joshua C.C., Eisenstat, Eric and Koop, Gary ORCID logoORCID: https://orcid.org/0000-0002-6091-378X;
  • Item type:Monograph(Discussion paper)
    ID code:68219
    Dates:
    Date
    Event
    25 September 2014
    Published
    Notes:Published as a paper within the Discussion Papers in Economics, No. 14-09 (2014)
    Subjects:Social Sciences > Economic Theory
    Department:Strathclyde Business School > Economics
    Depositing user: Pure Administrator
    Date deposited:04 Jun 2019 08:50
    Last modified:20 Nov 2024 16:48
    Related URLs:
    URI:https://strathprints.strath.ac.uk/id/eprint/68219
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