Composite Likelihood Methods for Large Bayesian VARs with Stochastic Volatility

Event 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-parameterisation 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 sub-models and then taking a weighted average across these sub-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 homoskedastic VARs.

Speaker biography

Gary Koop is a Professor in the Department of Economics at the University of Strathclyde. He received his BA, MA and PhD at the University of Toronto in 1983, 1984 and 1989, respectively. He has held professorial posts at the Universities of Edinburgh, Glasgow and Leicester and was an assistant professor at Boston University and the University of Toronto. His research work in Bayesian econometrics has resulted in over a hundred publications in international quality journals. He has also published several textbooks including Bayesian Econometrics, Bayesian Econometric Methods and is co-editor of the Oxford Handbook of Bayesian Econometrics. In addition, he is on the editorial board of several journals including the Journal of Business and Economic Statistics and the Journal of Applied Econometrics.