arXiv 18 Jul 2021 · Econometrics · 2 citations (OpenAlex)
arXiv:2107.08498 · PDF · DOI · OpenAlex · Extracted main text
This paper extends the idea of decoupling shrinkage and sparsity for continuous priors to Bayesian Quantile Regression (BQR). The procedure follows two steps: In the first step, we shrink the quantile regression posterior through state of the art continuous priors and in the second step, we sparsify the posterior through an efficient variant of the adaptive lasso, the signal adaptive variable selection (SAVS) algorithm. We propose a new variant of the SAVS which automates the choice of penalisation through quantile specific loss-functions that are valid in high dimensions. We show in large scale simulations that our selection procedure decreases bias irrespective of the true underlying degree of sparsity in the data, compared to the un-sparsified regression posterior. We apply our two-step approach to a high dimensional growth-at-risk (GaR) exercise. The prediction accuracy of the un-sparsified posterior is retained while yielding interpretable quantile specific variable selection results. Our procedure can be used to communicate to policymakers which variables drive downside risk to the macro economy.
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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 | Hahn, P. R. and C. M. Carvalho (2015) Decoupling shrinkage and selection in bayesian linear models: a posterior summary perspective | 1.000 | 7 | 3 | 100% |
| 2 | Adrian, T., N. Boyarchenko, and D. Giannone (2019) Vulnerable growth | 1.000 | 6 | 3 | 100% |
| 3 | Korobilis, D (2017) Quantile regression forecasts of inflation under model uncertainty | 1.000 | 5 | 3 | 100% |
| 4 | Koenker, R (2005) Quantile regression | 0.928 | 4 | 3 | 100% |
| 5 | Kohns, D. and T. Szendrei (2020) Horseshoe prior bayesian quantile regression self | 0.916 | 13 | 5 | 77% |
| 6 | Ray, P. and A. Bhattacharya (2018) Signal adaptive variable selector for the horseshoe prior | 0.874 | 6 | 3 | 67% |
| 7 | Zou, H (2006) The adaptive lasso and its oracle properties | 0.843 | 4 | 3 | 75% |
| 8 | Chernozhukov, V., I. Fernández-Val, and A. Galichon (2010) Quantile and probability curves without crossing | 0.811 | 4 | 2 | 100% |
| 9 | George, E. I. and R. E. McCulloch (1993) Variable selection via gibbs sampling | 0.737 | 4 | 3 | 50% |
| 10 | Carvalho, C. M., N. G. Polson, and J. G. Scott (2010) The horseshoe estimator for sparse signals | 0.737 | 3 | 2 | 100% |
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arXiv econ.EM papers that cite this one, ranked by how heavily they lean on it.