Bayesian Dynamic Tensor Regression

Monica Billio, Roberto Casarin, Matteo Iacopini and Sylvia Kaufmann

Journal of Business & Economic Statistics, 2023, vol. 41, issue 2, 429-439

Abstract: High- and multi-dimensional array data are becoming increasingly available. They admit a natural representation as tensors and call for appropriate statistical tools. We propose a new linear autoregressive tensor process (ART) for tensor-valued data, that encompasses some well-known time series models as special cases. We study its properties and derive the associated impulse response function. We exploit the PARAFAC low-rank decomposition for providing a parsimonious parameterization and develop a Bayesian inference allowing for shrinking effects. We apply the ART model to time series of multilayer networks and study the propagation of shocks across nodes, layers and time.

Date: 2023
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Citations: View citations in EconPapers (2)

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Working Paper: Bayesian Dynamic Tensor Regression (2018)
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DOI: 10.1080/07350015.2022.2032721

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