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Bayesian Dynamic Factor Models for High-dimensional Matrix-valued Time Series

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  • Wei Zhang

Abstract

We introduce a class of Bayesian matrix dynamic factor models that accommodates time-varying volatility, outliers, and cross-sectional correlation in the idiosyncratic components. For model comparison, we employ an importance-sampling estimator of the marginal likelihood based on the cross-entropy method to determine: (1) the optimal dimension of the factor matrix; (2) whether a vector- or matrix-valued structure is more suitable; and (3) whether an approximate or exact factor model is favored by the data. Through a series of Monte Carlo experiments, we demonstrate the accuracy of the factor estimates and the effectiveness of the marginal likelihood estimator in correctly identifying the true model. Applications to macroeconomic and financial datasets illustrate the model's ability to capture key features in matrix-valued time series.

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  • Wei Zhang, 2024. "Bayesian Dynamic Factor Models for High-dimensional Matrix-valued Time Series," Papers 2409.08354, arXiv.org, revised Aug 2025.
    • Handle: RePEc:arx:papers:2409.08354
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    References listed on IDEAS

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