Foundations and Trends® in Econometrics > Vol 3 > Issue 4

Bayesian Multivariate Time Series Methods for Empirical Macroeconomics

Gary Koop, Department of Economics, University of Strathclyde, UK, Gary.Koop@strath.ac.uk Dimitris Korobilis, Department of Economics, University of Strathclyde, UK, gcb07101@strath.ac.uk
 
Suggested Citation
Gary Koop and Dimitris Korobilis (2010), "Bayesian Multivariate Time Series Methods for Empirical Macroeconomics", Foundations and Trends® in Econometrics: Vol. 3: No. 4, pp 267-358. http://dx.doi.org/10.1561/0800000013

Published: 20 Jul 2010
© 2010 G. Koop and D. Korobilis
 
Subjects
Econometric models
 
Keywords
C11 Bayesian AnalysisC1 Econometrics and Statistical MethodsGeneralE0 MacroeconomicsGeneral
Multivariate time series modelsBayesian methodsVARsEmpirical macroeconomicsEconometricsMacroeconomicsFinance
 

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In this article:
1. Introduction
2. Bayesian VARs
3. Bayesian State Space Modeling and Stochastic Volatility
4. TVP–VARs
5. Factor Methods
6. Conclusion
References

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 offer 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.

DOI:10.1561/0800000013
ISBN: 978-1-60198-362-6
100 pp. $75.00
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ISBN: 978-1-60198-363-3
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Table of contents:
1. Introduction
2. Bayesian VARs
3. Bayesian State Space Modeling and Stochastic Volatility
4. TVP-VARs
5. Factor Methods
6. Conclusion
References

Bayesian Multivariate Time Series Methods for Empirical Macroeconomics

Bayesian Multivariate Time Series Methods for Empirical Macroeconomics provides a survey of the Bayesian methods used in modern empirical macroeconomics. These models have been developed to address the fact that most questions of interest to empirical macroeconomists involve several variables and must be addressed using multivariate time series methods. Many different multivariate time series models have been used in macroeconomics, but Vector Autoregressive (VAR) models have been among the most popular.

Bayesian Multivariate Time Series Methods for Empirical Macroeconomics reviews and extends the Bayesian literature on VARs, TVP-VARs and TVP-FAVARs with a focus on the practitioner. The authors go beyond simply defining each model, but specify how to use them in practice, discuss the advantages and disadvantages of each and offer tips on when and why each model can be used.

 
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