arXiv 10 Jun 2022 · Econometrics · publishedInternational Journal of Forecasting (2025) · 10 citations (OpenAlex)
arXiv:2206.04902 · PDF · DOI · OpenAlex · Extracted main text
Vector autogressions (VARs) are widely applied when it comes to modeling and forecasting macroeconomic variables. In high dimensions, however, they are prone to overfitting. Bayesian methods, more concretely shrinkage priors, have shown to be successful in improving prediction performance. In the present paper, we introduce the semi-global framework, in which we replace the traditional global shrinkage parameter with group-specific shrinkage parameters. We show how this framework can be applied to various shrinkage priors, such as global-local priors and stochastic search variable selection priors. We demonstrate the virtues of the proposed framework in an extensive simulation study and in an empirical application forecasting data of the US economy. Further, we shed more light on the ongoing “Illusion of Sparsity” debate, finding that forecasting performances under sparse/dense priors vary across evaluated economic variables and across time frames. Dynamic model averaging, however, can combine the merits of both worlds.
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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 | Litterman, R. B (1986) Forecasting with Bayesian vector autoregressions: Five years of experience | 0.956 | 8 | 5 | 88% |
| 2 | Chan, J. C. C (2021) Minnesota-type adaptive hierarchical priors for large Bayesian VARs | 0.956 | 8 | 4 | 88% |
| 3 | George, E. I., Sun, D., and Ni, S (2008) Bayesian stochastic search for VAR model restrictions | 0.941 | 6 | 4 | 83% |
| 4 | Kastner, G. and Huber, F (2020) Sparse Bayesian vector autoregressions in huge dimensions self | 0.935 | 11 | 4 | 82% |
| 5 | Carriero, A., Clark, T. E., and Marcellino, M (2019) Large Bayesian vector autoregressions with stochastic volatility and non-conjugate priors | 0.928 | 4 | 3 | 100% |
| 6 | Brown, P. J. and Griffin, J. E (2010) Inference with normal-gamma prior distributions in regression problems | 0.928 | 4 | 3 | 100% |
| 7 | Cross, J. L., Hou, C., and Poon, A (2020) Macroeconomic forecasting with large Bayesian VARs: Global-local priors and the illusion of sparsity | 0.916 | 13 | 4 | 77% |
| 8 | Huber, F. and Feldkircher, M (2019) Adaptive shrinkage in Bayesian vector autoregressive models | 0.902 | 15 | 5 | 73% |
| 9 | Carriero, A., Chan, J., Clark, T. E., and Marcellino, M (2022) Corrigendum to “Large Bayesian vector autoregressions with stochastic volatility and non-conjugate priors” [J. Econometrics 212… | 0.843 | 4 | 3 | 75% |
| 10 | Zhang, Y. D., Naughton, B. P., Bondell, H. D., and Reich, B. J (2022) Bayesian regression using a prior on the model fit: The R2-D2 shrinkage prior | 0.843 | 10 | 6 | 60% |
Showing the top 10 of 69 scored citations.
arXiv econ.EM papers that cite this one, ranked by how heavily they lean on it.
| Citing paper | Intensity | Mentions | Sections | |
|---|---|---|---|---|
| 1 | Bayesian Shrinkage in High-Dimensional VAR Models: A Comparative Study | 0.405 | 1 | 1 |