arXiv 22 Oct 2023 · Econometrics · 1 citations (OpenAlex)
arXiv:2310.14438 · PDF · DOI · OpenAlex · Extracted main text
Bayesian vector autoregressions (BVARs) are the workhorse in macroeconomic forecasting. Research in the last decade has established the importance of allowing time-varying volatility to capture both secular and cyclical variations in macroeconomic uncertainty. This recognition, together with the growing availability of large datasets, has propelled a surge in recent research in building stochastic volatility models suitable for large BVARs. Some of these new models are also equipped with additional features that are especially desirable for large systems, such as order invariance -- i.e., estimates are not dependent on how the variables are ordered in the BVAR -- and robustness against COVID-19 outliers. Estimation of these large, flexible models is made possible by the recently developed equation-by-equation approach that drastically reduces the computational cost of estimating large systems. Despite these recent advances, there remains much ongoing work, such as the development of parsimonious approaches for time-varying coefficients and other types of nonlinearities in large BVARs.
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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 | Primiceri (2005) Time Varying Structural Vector Autoregressions and Monetary Policy | 1.000 | 12 | 5 | 100% |
| 2 | Cogley and Sargent (2005) Drifts and volatilities: Monetary policies and outcomes in the post WWII US | 1.000 | 8 | 5 | 100% |
| 3 | Carriero, Clark, and Marcellino (2019) Large Bayesian vector autoregressions with stochastic volatility and non-conjugate priors | 1.000 | 5 | 4 | 100% |
| 4 | Carriero, Clark, and Marcellino (2016) Common drifting volatility in large Bayesian VARs | 1.000 | 5 | 3 | 100% |
| 5 | Chan and Eisenstat (2018) Bayesian Model Comparison for Time-Varying Parameter VARs with Stochastic Volatility | 0.928 | 4 | 3 | 100% |
| 6 | Hauzenberger, Huber, Koop, and Mitchell (2022) Bayesian modeling of time-varying parameters using regression trees | 0.843 | 3 | 3 | 100% |
| 7 | Chan (2020) Large Bayesian VARs: A Flexible Kronecker Error Covariance Structure self | 0.843 | 3 | 3 | 100% |
| 8 | Carriero, Clark, Marcellino, and Mertens (2022) Addressing COVID-19 outliers in BVARs with stochastic volatility | 0.811 | 4 | 2 | 100% |
| 9 | Cross, Hou, Koop, and Poon (2023) Large stochastic volatility in mean VARs | 0.737 | 3 | 2 | 100% |
| 10 | Chan, Koop, and Yu (2023) Large order-invariant Bayesian VARs with stochastic volatility | 0.737 | 3 | 2 | 100% |
Showing the top 10 of 151 scored citations.
arXiv econ.EM papers that cite this one, ranked by how heavily they lean on it.
| Citing paper | Intensity | Mentions | Sections | |
|---|---|---|---|---|
| 1 | Large Bayesian Tensor VARs with Stochastic Volatility | 0.405 | 1 | 1 |