Large Bayesian Tensor VARs with Stochastic Volatility

Joshua C. C. Chan () and Yaling Qi ()
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Joshua C. C. Chan: Purdue University, Economics Department
Yaling Qi: Purdue University, Economics Department

A chapter in Recent Developments in Bayesian Econometrics and Their Applications, 2025, pp 23-45 from Springer

Abstract: Abstract We consider Bayesian tensor vector autoregressions (TVARs) in which the VAR coefficients are arranged as a three-dimensional array or tensor, and this coefficient tensor is parameterized using a low-rank CP (CANDECOMP/PARAFAC) decomposition. We develop a family of TVARs using a general stochastic volatility specification, which includes a wide variety of commonly used multivariate stochastic volatility and COVID-19 outlier-augmented models. In a forecasting exercise involving 40 US quarterly variables, we show that these TVARs outperform the standard Bayesian VAR with the Minnesota prior. The results also suggest that the parsimonious common stochastic volatility model tends to forecast better than the more flexible Cholesky stochastic volatility model.

Date: 2025
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Persistent link: https://EconPapers.repec.org/RePEc:spr:sprchp:978-3-032-00110-8_3

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DOI: 10.1007/978-3-032-00110-8_3

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