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Large Bayesian matrix autoregressions
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DOI：10.1016/j.jeconom.2025.105955.png)
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Abstract
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High-dimensional matrix-valued time-series are increasingly common in economics and finance. Prominent examples include large cross-region panels and dynamic economic networks. As the dimensions of the matrix grow, conventional approaches based on vector autoregressions—implemented by vectoring the matrix-valued data—become computationally infeasible. We introduce a class of large Bayesian matrix autoregressions (BMARs) that can accommodate time-varying volatility, non-Gaussian errors and COVID-19 outliers. To tackle parameter proliferation, we propose Minnesota-type shrinkage priors on the MAR coefficients. We develop a unified approach for estimating this class of models, which scales well to high dimensions. The empirical relevance of these new BMARs is illustrated using a US state-level dataset that contains 6 macroeconomic times-series for each of the 50 states, with a total of 300 times-series.
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