Bayesian Multivariate Time Series Methods for Empirical Macroeconomics

65 Pages Posted: 30 Nov 2009

See all articles by Gary Koop

Gary Koop

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

Dimitris Korobilis

University of Glasgow - Adam Smith Business School

Date Written: September 20, 2009

Abstract

Macroeconomic practitioners frequently work with multivariate time series models such as VARs, factor augmented VARs as well as time-varying parameter versions of these models (including variants with multivariate stochastic volatility). These models have a large number of parameters and, thus, over-parameterization problems may arise. Bayesian methods have become increasingly popular as a way of overcoming these problems. In this monograph, we discuss VARs, factor augmented VARs and time-varying parameter extensions and show how Bayesian inference proceeds. Apart from the simplest of VARs, Bayesian inference requires the use of Markov chain Monte Carlo methods developed for state space models and we describe these algorithms. The focus is on the empirical macroeconomist and we o¤er advice on how to use these models and methods in practice and include empirical illustrations. A website provides Matlab code for carrying out Bayesian inference in these models.

Suggested Citation

Koop, Gary and Korobilis, Dimitris, Bayesian Multivariate Time Series Methods for Empirical Macroeconomics (September 20, 2009). Available at SSRN: https://ssrn.com/abstract=1514412 or http://dx.doi.org/10.2139/ssrn.1514412

Gary Koop

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

100 Cathedral Street
Glasgow G4 0LN
United Kingdom

Dimitris Korobilis (Contact Author)

University of Glasgow - Adam Smith Business School ( email )

40 University Avenue
Gilbert Scott Building
Glasgow, Scotland G12 8QQ
United Kingdom

HOME PAGE: http://https://sites.google.com/site/dimitriskorobilis/

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