Analyzing Multiple Vector Autoregressions Through Matrix-Variate Normal Distribution with Two Covariance Matrices

27 Pages Posted: 9 Nov 2017

See all articles by Nuttanan Wichitaksorn

Nuttanan Wichitaksorn

Auckland University of Technology; University of Canterbury; Thailand Development Research Institute

Date Written: Septmeber 20, 2017

Abstract

This paper proposes a new approach to analyze multiple vector autoregressive (VAR) models that render us a newly constructed matrix autoregressive (MtAR) model based on a matrix-variate normal distribution with two covariance matrices. The MtAR is a generalization of VAR models where the two covariance matrices allow the extension of MtAR to a structural MtAR analysis. The proposed MtAR can also incorporate different lag orders across VAR systems that provide more flexibility to the model. The estimation results from a simulation study and an empirical study on macroeconomic application show favorable performance of our proposed models and method.

Keywords: Markov chain Monte Carlo, Multivariate analysis, Matrix-variate normal distribution, Autoregression

JEL Classification: C11, C13, C32, E39, E49

Suggested Citation

Wichitaksorn, Nuttanan, Analyzing Multiple Vector Autoregressions Through Matrix-Variate Normal Distribution with Two Covariance Matrices (Septmeber 20, 2017). Available at SSRN: https://ssrn.com/abstract=3066981 or http://dx.doi.org/10.2139/ssrn.3066981

Nuttanan Wichitaksorn (Contact Author)

Auckland University of Technology ( email )

AUT City Campus
Private Bag 92006
Auckland, 1142
New Zealand

University of Canterbury ( email )

Ilam Road
Christchurch 8140
New Zealand

Thailand Development Research Institute ( email )

Bangkok
Thailand

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