Composite Likelihood Methods for Large Bayesian VARs with Stochastic Volatility

44 Pages Posted: 3 Jun 2018

See all articles by Joshua Chan

Joshua Chan

University of Technology Sydney (UTS)

Eric Eisenstat

Eisenstat

Chenghan Hou

Hunan University - Center for Economics, Finance and Management Studies

Gary Koop

University of Strathclyde, Glasgow - Strathclyde Business School - Department of Economics

Date Written: May 29, 2018

Abstract

Adding multivariate stochastic volatility of a flexible form to large Vector Autoregressions (VARs) involving over a hundred variables has proved challenging due to computational considerations and over-parameterization concerns. The existing literature either works with homoskedastic models or smaller models with restrictive forms for the stochastic volatility. In this paper, we develop composite likelihood methods for large VARs with multivariate stochastic volatility. These involve estimating large numbers of parsimonious models and then taking a weighted average across these models. We discuss various schemes for choosing the weights. In our empirical work involving VARs of up to 196 variables, we show that composite likelihood methods have similar properties to existing alternatives used with small data sets in that they estimate the multivariate stochastic volatility in a flexible and realistic manner and they forecast comparably. In very high dimensional VARs, they are computationally feasible where other approaches involving stochastic volatility are not and produce superior forecasts than natural conjugate prior homoscedastic VARs.

Keywords: Bayesian, large VAR, composite likelihood, prediction pools, stochastic volatility

JEL Classification: C11, C32, C53

Suggested Citation

Chan, Joshua and Eisenstat, Eric and Hou, Chenghan and Koop, Gary, Composite Likelihood Methods for Large Bayesian VARs with Stochastic Volatility (May 29, 2018). CAMA Working Paper No. 26/2018. Available at SSRN: https://ssrn.com/abstract=3187049 or http://dx.doi.org/10.2139/ssrn.3187049

Joshua Chan (Contact Author)

University of Technology Sydney (UTS) ( email )

15 Broadway, Ultimo
PO Box 123
Sydney, NSW 2007
Australia

Eric Eisenstat

Eisenstat ( email )

St Lucia
Brisbane, Queensland 4072
Australia

Chenghan Hou

Hunan University - Center for Economics, Finance and Management Studies ( email )

2 Lushan South Rd
Changsha, Hunan 410082
China

Gary Koop

University of Strathclyde, Glasgow - Strathclyde Business School - Department of Economics ( email )

100 Cathedral Street
Glasgow G4 0LN
United Kingdom

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