Analyzing multiple vector autoregressions through matrix-variate normal distribution with two covariance matrices

Nuttanan Wichitaksorn

Communications in Statistics - Theory and Methods, 2020, vol. 49, issue 8, 1801-1817

Abstract: This article 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.

Date: 2020
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Persistent link: https://EconPapers.repec.org/RePEc:taf:lstaxx:v:49:y:2020:i:8:p:1801-1817

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DOI: 10.1080/03610926.2019.1565832

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