EconPapers    
Economics at your fingertips  
 

Composite Likelihood Methods for Large Bayesian VARs with Stochastic Volatility

Joshua Chan, Eric Eisenstat, Chenghan Hou and Gary Koop ()
Additional contact information
Chenghan Hou: Hunan University

No 44, Working Paper Series from Economics Discipline Group, UTS Business School, University of Technology, Sydney

Abstract: Adding multivariate stochastic volatility of a ?exible 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 pa- per, 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 ?exible 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.

Keywords: Bayesian; large VAR; composite likelihood; prediction pools; stochastic volatility (search for similar items in EconPapers)
JEL-codes: C11 C32 C53 (search for similar items in EconPapers)
Pages: 42 pages
Date: 2018-05-01
New Economics Papers: this item is included in nep-for and nep-ore
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (4) Track citations by RSS feed

Downloads: (external link)
https://www.uts.edu.au/sites/default/files/2018-07 ... ds%20for%20Large.pdf (application/pdf)

Related works:
Working Paper: Composite likelihood methods for large Bayesian VARs with stochastic volatility (2018) Downloads
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:uts:ecowps:44

Access Statistics for this paper

More papers in Working Paper Series from Economics Discipline Group, UTS Business School, University of Technology, Sydney PO Box 123, Broadway, NSW 2007, Australia. Contact information at EDIRC.
Bibliographic data for series maintained by Duncan Ford ().

 
Page updated 2020-01-22
Handle: RePEc:uts:ecowps:44