arXiv 16 Jun 2022 · Econometrics · publishedJournal of Economic Dynamics and Control (2022) · 13 citations (OpenAlex)
arXiv:2206.08438 · PDF · DOI · OpenAlex · Extracted main text
We propose a new variational approximation of the joint posterior distribution of the log-volatility in the context of large Bayesian VARs. In contrast to existing approaches that are based on local approximations, the new proposal provides a global approximation that takes into account the entire support of the joint distribution. In a Monte Carlo study we show that the new global approximation is over an order of magnitude more accurate than existing alternatives. We illustrate the proposed methodology with an application of a 96-variable VAR with stochastic volatility to measure global bank network connectedness.
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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 | Cogley and Sargent (2005) Drifts and volatilities: Monetary policies and outcomes in the post WWII US | 0.928 | 4 | 3 | 100% |
| 2 | Gefang, Koop, and Poon (2019) Variational Bayesian inference in large Vector Autoregressions with hierarchical shrinkage | 0.928 | 4 | 3 | 100% |
| 3 | Koop and Korobilis (2018) Variational Bayes inference in high-dimensional time-varying parameter models | 0.928 | 4 | 3 | 100% |
| 4 | Primiceri (2005) Time Varying Structural Vector Autoregressions and Monetary Policy | 0.843 | 3 | 3 | 100% |
| 5 | Carriero, Clark, and Marcellino (2019) Large Bayesian vector autoregressions with stochastic volatility and non-conjugate priors | 0.737 | 3 | 2 | 100% |
| 6 | Demirer, Diebold, Liu, and Yilmaz (2018) Estimating global bank network connectedness | 0.737 | 3 | 2 | 100% |
| 7 | Arias, Rubio-Ramirez, and Shin (2021) Macroeconomic forecasting and variable ordering in multivariate stochastic volatility models | 0.644 | 2 | 2 | 100% |
| 8 | Carriero, Clark, and Marcellino (2015) Bayesian VARs: Specification Choices and Forecast Accuracy | 0.644 | 2 | 2 | 100% |
| 9 | Loaiza-Maya, Smith, Nott, and Danaher (2020) Fast and Accurate Variational Inference for Models with Many Latent Variables | 0.644 | 2 | 2 | 100% |
| 10 | Diebold and Yilmaz (2014) On the network topology of variance decompositions: Measuring the connectedness of financial firms | 0.585 | 3 | 1 | 100% |
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