Mauro Bernardi, Daniele Bianchi, Nicolas Bianco
arXiv 25 Feb 2022 · Econometrics · publishedJournal of Business and Economic Statistics (2023) · 11 citations (OpenAlex)
arXiv:2202.12644 · PDF · DOI · OpenAlex · Extracted main text
We propose a novel variational Bayes approach to estimate high-dimensional vector autoregression (VAR) models with hierarchical shrinkage priors. Our approach does not rely on a conventional structural VAR representation of the parameter space for posterior inference. Instead, we elicit hierarchical shrinkage priors directly on the matrix of regression coefficients so that (1) the prior structure directly maps into posterior inference on the reduced-form transition matrix, and (2) posterior estimates are more robust to variables permutation. An extensive simulation study provides evidence that our approach compares favourably against existing linear and non-linear Markov Chain Monte Carlo and variational Bayes methods. We investigate both the statistical and economic value of the forecasts from our variational inference approach within the context of a mean-variance investor allocating her wealth in a large set of different industry portfolios. The results show that more accurate estimates translate into substantial statistical and economic out-of-sample gains. The results hold across different hierarchical shrinkage priors and model dimensions.
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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 | J. C. Chan and X. Yu (2022) Fast and accurate variational inference for large bayesian VARs with stochastic volatility | 1.000 | 7 | 3 | 100% |
| 2 | J. C. Chan and E. Eisenstat (2018) Bayesian model comparison for time-varying parameter vars with stochastic volatility | 1.000 | 5 | 3 | 100% |
| 3 | J. L. Cross, C. Hou, and A. Poon (2020) Macroeconomic forecasting with large Bayesian VARs: Global-local priors and the illusion of sparsity | 1.000 | 5 | 3 | 100% |
| 4 | D. Gefang, G. Koop, and A. Poon (2023) Forecasting using variational bayesian inference in large vector autoregressions with hierarchical shrinkage | 0.953 | 15 | 7 | 87% |
| 5 | F. Huber and M. Feldkircher (2019) Adaptive shrinkage in bayesian vector autoregressive models | 0.928 | 5 | 4 | 80% |
| 6 | L. Gruber and G. Kastner (2022) Forecasting macroeconomic data with bayesian VARs: Sparse or dense? it depends! | 0.890 | 17 | 7 | 71% |
| 7 | P. Ray and A. Bhattacharya (2018) Signal adaptive variable selector for the horseshoe prior | 0.843 | 4 | 3 | 75% |
| 8 | A. Carriero, J. Chan, T. E. Clark, and M. Marcellino (2022) Corrigendum to “large bayesian vector autoregressions with stochastic volatility and non-conjugate priors”[j. econometrics 212 (… | 0.737 | 4 | 3 | 50% |
| 9 | A. Carriero, T. E. Clark, and M. Marcellino (2019) Large bayesian vector autoregressions with stochastic volatility and non-conjugate priors | 0.737 | 3 | 3 | 67% |
| 10 | P. R. Hahn and C. M. Carvalho (2015) Decoupling shrinkage and selection in bayesian linear models: a posterior summary perspective | 0.675 | 13 | 3 | 31% |
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