Large Bayesian VARMAs

Joshua C.C. Chan, Eric Eisenstat, Gary Koop

Research output: Contribution to journalArticle

4 Citations (Scopus)

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.
LanguageEnglish
JournalJournal of Econometrics
Volume192
Issue number2
Early online date11 Feb 2016
DOIs
Publication statusPublished - 1 Jun 2016

Fingerprint

Markov chain Monte Carlo
Autoregressive moving average model
Vector autoregressive
Bayesian approach
Inference
Multivariate time series
Macroeconomics
Parsimony
Time series models

Keywords

  • VARMA identification
  • Markov chain Monte Carlo
  • Bayesian
  • stochastic search variable selection

Cite this

Chan, Joshua C.C. ; Eisenstat, Eric ; Koop, Gary. / Large Bayesian VARMAs. In: Journal of Econometrics. 2016 ; Vol. 192, No. 2.
@article{acea254289a84db8bae618d6b04d16e5,
title = "Large Bayesian VARMAs",
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.",
keywords = "VARMA identification, Markov chain Monte Carlo, Bayesian, stochastic search variable selection",
author = "Chan, {Joshua C.C.} and Eric Eisenstat and Gary Koop",
year = "2016",
month = "6",
day = "1",
doi = "10.1016/j.jeconom.2016.02.005",
language = "English",
volume = "192",
journal = "Journal of Econometrics",
issn = "0304-4076",
number = "2",

}

Large Bayesian VARMAs. / Chan, Joshua C.C.; Eisenstat, Eric ; Koop, Gary.

In: Journal of Econometrics, Vol. 192, No. 2, 01.06.2016.

Research output: Contribution to journalArticle

TY - JOUR

T1 - Large Bayesian VARMAs

AU - Chan, Joshua C.C.

AU - Eisenstat, Eric

AU - Koop, Gary

PY - 2016/6/1

Y1 - 2016/6/1

N2 - 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.

AB - 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.

KW - VARMA identification

KW - Markov chain Monte Carlo

KW - Bayesian

KW - stochastic search variable selection

UR - http://www.sciencedirect.com/science/article/pii/S0304407616300082

U2 - 10.1016/j.jeconom.2016.02.005

DO - 10.1016/j.jeconom.2016.02.005

M3 - Article

VL - 192

JO - Journal of Econometrics

T2 - Journal of Econometrics

JF - Journal of Econometrics

SN - 0304-4076

IS - 2

ER -