Template-Type: ReDIF-Paper 1.0
Author-Name: Joshua  C C  Chan
Author-Name-First: Joshua 
Author-Name-Last: C C  Chan
Author-Email: joshuacc.chan@gmail.com
Author-Workplace-Name: Australian National University
Author-Name: Eric  Eisenstat
Author-Name-First: Eric 
Author-Name-Last: Eisenstat
Author-Email: eric.eisenstat@gmail.com
Author-Workplace-Name: University of Bucharest
Author-Name: Gary Koop
Author-Name-First: Gary
Author-Name-Last: Koop
Author-Email: gary.koop@strath.ac.uk
Author-Workplace-Name: Department of Economics, University of Strathclyde
Title: Large Bayesian VARMAs
Abstract: Abstract:  Vector Autoregressive Moving Average (VARMA) models have many theoretical properties which should make them popular among empirical macroeconomists. However, they are rarely used in practice due to over-parameterization concerns, difficult - ties in ensuring identification and computational challenges. With the growing interest in multivariate time series models of high dimension, these problems with VARMAs become even more acute, accounting for the dominance of VARs in this field.  In this paper, we develop a Bayesian approach for inference in VARMAs which surmounts these problems. It jointly ensures identification and parsimony in the context of an efficient Markov chain Monte Carlo (MCMC) algorithm.  We use this approach in a macroeconomic application involving up to twelve dependent variables.  We find our algorithm to work successfully and provide insights beyond those provided by VARs
Length: 43 pages
Creation-Date: 2014-09
Revision-Date: 
Publication-Status: Published
File-URL: http://www.strath.ac.uk/media/1newwebsite/departmentsubject/economics/research/researchdiscussionpapers/14-09.pdf
File-Format: Application/pdf
Number: 1409
Classification-JEL: C11, C32, E37
Keywords: VARMA identification, Markov Chain Monte Carlo, Bayesian, stochastic search variable selection
Handle: RePEc:str:wpaper:1409