Bayesian Multivariate Time Series Methods for Empirical Macroeconomics
Gary Koop () and
Working Paper series from Rimini Centre for Economic Analysis
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.
Keywords: Empirical macroeconometrics; Bayesian estimation; MCMC; vector autoregressions; factor models; time-varying parameters (search for similar items in EconPapers)
JEL-codes: C11 C13 C15 C51 C52 C53 C87 E52 (search for similar items in EconPapers)
References: Add references at CitEc
Citations: View citations in EconPapers (116) Track citations by RSS feed
Downloads: (external link)
Journal Article: Bayesian Multivariate Time Series Methods for Empirical Macroeconomics (2010)
Working Paper: Bayesian Multivariate Time Series Methods for Empirical Macroeconomics (2009)
This item may be available elsewhere in EconPapers: Search for items with the same title.
Export reference: BibTeX
RIS (EndNote, ProCite, RefMan)
Persistent link: https://EconPapers.repec.org/RePEc:rim:rimwps:47_09
Access Statistics for this paper
More papers in Working Paper series from Rimini Centre for Economic Analysis Contact information at EDIRC.
Bibliographic data for series maintained by Marco Savioli ().