arXiv 28 Aug 2022 · Econometrics · publishedJournal of Econometrics (2022) · 37 citations (OpenAlex)
arXiv:2208.13255 · PDF · DOI · OpenAlex · Extracted main text
Large Bayesian vector autoregressions with various forms of stochastic volatility have become increasingly popular in empirical macroeconomics. One main difficulty for practitioners is to choose the most suitable stochastic volatility specification for their particular application. We develop Bayesian model comparison methods -- based on marginal likelihood estimators that combine conditional Monte Carlo and adaptive importance sampling -- to choose among a variety of stochastic volatility specifications. The proposed methods can also be used to select an appropriate shrinkage prior on the VAR coefficients, which is a critical component for avoiding over-fitting in high-dimensional settings. Using US quarterly data of different dimensions, we find that both the Cholesky stochastic volatility and factor stochastic volatility outperform the common stochastic volatility specification. Their superior performance, however, can mostly be attributed to the more flexible priors that accommodate cross-variable shrinkage.
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The works this paper leans on most, across its whole bibliography — not restricted to papers in our corpus. Ranked by composite intensity, which combines how often a work is mentioned, how many sections mention it, and how much of that falls in the main text rather than the appendix.
| Reference | Intensity | Mentions | Sections | Main text | |
|---|---|---|---|---|---|
| 1 | Carriero, Clark, and Marcellino (2015) Bayesian VARs: Specification Choices and Forecast Accuracy | 1.000 | 5 | 3 | 100% |
| 2 | Carriero, Clark, and Marcellino (2016) Common drifting volatility in large Bayesian VARs | 1.000 | 5 | 3 | 100% |
| 3 | Chan (2021) Minnesota-type adaptive hierarchical priors for large Bayesian VARs self | 0.928 | 5 | 3 | 80% |
| 4 | Giannone, Lenza, and Primiceri (2015) Prior selection for vector autoregressions | 0.928 | 4 | 3 | 100% |
| 5 | Chan (2020) Large Bayesian VARs: A Flexible Kronecker Error Covariance Structure self | 0.855 | 8 | 5 | 62% |
| 6 | Chan and Eisenstat (2015) Marginal Likelihood Estimation with the Cross-Entropy Method | 0.811 | 5 | 2 | 80% |
| 7 | Carriero, Clark, and Marcellino (2019) Large Bayesian vector autoregressions with stochastic volatility and non-conjugate priors | 0.811 | 4 | 2 | 100% |
| 8 | Chan and Eisenstat (2018) Bayesian Model Comparison for Time-Varying Parameter VARs with Stochastic Volatility | 0.737 | 3 | 2 | 100% |
| 9 | Kastner (2019) Sparse Bayesian time-varying covariance estimation in many dimensions | 0.737 | 3 | 2 | 100% |
| 10 | Carriero, Clark, Marcellino, and Mertens (2022) Addressing COVID-19 outliers in BVARs with stochastic volatility | 0.644 | 4 | 1 | 100% |
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