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

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