arXiv 13 Nov 2021 · Econometrics · publishedQuantitative Economics (2022) · 44 citations (OpenAlex)
arXiv:2111.07170 · PDF · DOI · OpenAlex · Extracted main text
Large Bayesian VARs are now widely used in empirical macroeconomics. One popular shrinkage prior in this setting is the natural conjugate prior as it facilitates posterior simulation and leads to a range of useful analytical results. This is, however, at the expense of modeling flexibility, as it rules out cross-variable shrinkage -- i.e., shrinking coefficients on lags of other variables more aggressively than those on own lags. We develop a prior that has the best of both worlds: it can accommodate cross-variable shrinkage, while maintaining many useful analytical results, such as a closed-form expression of the marginal likelihood. This new prior also leads to fast posterior simulation -- for a BVAR with 100 variables and 4 lags, obtaining 10,000 posterior draws takes less than half a minute on a standard desktop. We demonstrate the usefulness of the new prior via a structural analysis using a 15-variable VAR with sign restrictions to identify 5 structural shocks.
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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 (2019) Large Bayesian vector autoregressions with stochastic volatility and non-conjugate priors | 1.000 | 8 | 3 | 100% |
| 2 | Carriero, Clark, and Marcellino (2015) Bayesian VARs: Specification Choices and Forecast Accuracy | 1.000 | 6 | 3 | 100% |
| 3 | Furlanetto, Ravazzolo, and Sarferaz (2019) Identification of financial factors in economic fluctuations | 0.874 | 12 | 2 | 100% |
| 4 | Rubio-Ramirez, Waggoner, and Zha (2010) Structural vector autoregressions: Theory of identification and algorithms for inference | 0.737 | 4 | 2 | 75% |
| 5 | Sims and Zha (1998) Bayesian methods for dynamic multivariate models | 0.644 | 4 | 1 | 100% |
| 6 | Del Negro and Schorfheide (2004) Priors from General Equilibrium Models for VARs | 0.644 | 2 | 2 | 100% |
| 7 | Schorfheide and Song (2015) Real-Time Forecasting With a Mixed-Frequency VAR | 0.644 | 2 | 2 | 100% |
| 8 | Chan (2020) Large Bayesian VARs: A Flexible Kronecker Error Covariance Structure self | 0.644 | 2 | 2 | 100% |
| 9 | Chan and Jeliazkov (2009) MCMC Estimation of Restricted Covariance Matrix self | 0.511 | 2 | 2 | 50% |
| 10 | Litterman (1986) Forecasting With Bayesian Vector Autoregressions –- Five Years of Experience | 0.511 | 2 | 2 | 50% |
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