arXiv 24 Sep 2024 · Econometrics
arXiv:2409.16132 · PDF · DOI · OpenAlex · Extracted main text
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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| Reference | Intensity | Mentions | Sections | Main text | |
|---|---|---|---|---|---|
| 1 | Cogley and Sargent (2005) Drifts and volatilities: Monetary policies and outcomes in the post WWII US | 0.811 | 4 | 2 | 100% |
| 2 | Wang, Zheng, Lian, and Li (2022) High-dimensional vector autoregressive time series modeling via tensor decomposition | 0.737 | 3 | 2 | 100% |
| 3 | Carriero, Chan, Clark, and Marcellino (2022) Corrigendum to “Large Bayesian vector autoregressions with stochastic volatility and non-conjugate priors" | 0.644 | 2 | 2 | 100% |
| 4 | Carriero, Clark, and Marcellino (2016) Common drifting volatility in large Bayesian VARs | 0.644 | 2 | 2 | 100% |
| 5 | Carriero, Clark, and Marcellino (2019) Large Bayesian vector autoregressions with stochastic volatility and non-conjugate priors | 0.644 | 2 | 2 | 100% |
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| 7 | Carriero, Kapetanios, and Marcellino (2011) Forecasting large datasets with Bayesian reduced rank multivariate models | 0.644 | 2 | 2 | 100% |
| 8 | D'Agostino, Gambetti, and Giannone (2013) Macroeconomic forecasting and structural change | 0.644 | 2 | 2 | 100% |
| 9 | Doan, Litterman, and Sims (1984) Forecasting and conditional projection using realistic prior distributions | 0.644 | 2 | 2 | 100% |
| 10 | Giannone, Lenza, and Primiceri (2015) Prior selection for vector autoregressions | 0.644 | 2 | 2 | 100% |
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