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Large Bayesian Tensor VARs with Stochastic Volatility
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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 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.
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Tensor Stochastic Regression for High-dimensional Time Series via CP Decomposition
A CP low-rank tensor stochastic regression model for time series is proposed with sparse and non-sparse estimators, theoretical error bounds, and applications to macroeconomic and air pollution data.
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