arXiv 13 Jun 2020 · Econometrics · publishedJournal of the Royal Statistical Society Series C (Applied Statistics) (2023) · 4 citations (OpenAlex)
arXiv:2006.07655 · PDF · DOI · OpenAlex · Extracted main text
This paper extends the horseshoe prior of Carvalho et al. (2010) to Bayesian quantile regression (HS-BQR) and provides a fast sampling algorithm for computation in high dimensions. The performance of the proposed HS-BQR is evaluated on Monte Carlo simulations and a high dimensional Growth-at-Risk (GaR) forecasting application for the U.S. The Monte Carlo design considers several sparsity and error structures. Compared to alternative shrinkage priors, the proposed HS-BQR yields better (or at worst similar) performance in coefficient bias and forecast error. The HS-BQR is particularly potent in sparse designs and in estimating extreme quantiles. As expected, the simulations also highlight that identifying quantile specific location and scale effects for individual regressors in dense DGPs requires substantial data. In the GaR application, we forecast tail risks as well as complete forecast densities using the McCracken and Ng (2020) database. Quantile specific and density calibration score functions show that the HS-BQR provides the best performance, especially at short and medium run horizons. The ability to produce well calibrated density forecasts and accurate downside risk measures in large data contexts makes the HS-BQR a promising tool for nowcasting applications and recession modelling.
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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 | Carvalho, C. M., N. G. Polson, and J. G. Scott (2010) The horseshoe estimator for sparse signals | 1.000 | 7 | 4 | 100% |
| 2 | Carriero, A., T. E. Clark, and M. G. Marcellino (2020) Nowcasting tail risks to economic activity with many indicators | 0.928 | 4 | 3 | 100% |
| 3 | Koenker, R (2005) Quantile regression | 0.928 | 4 | 3 | 100% |
| 4 | Mazzi, G. L. and J. Mitchell (2019) Nowcasting euro area gdp growth using quantile regression | 0.928 | 4 | 3 | 100% |
| 5 | Adrian, T., N. Boyarchenko, and D. Giannone (2019) Vulnerable growth | 0.874 | 5 | 2 | 100% |
| 6 | Kozumi, H. and G. Kobayashi (2011) Gibbs sampling methods for bayesian quantile regression | 0.874 | 5 | 2 | 100% |
| 7 | Yu, K. and R. A. Moyeed (2001) Bayesian quantile regression | 0.811 | 4 | 2 | 100% |
| 8 | Bhattacharya, A., A. Chakraborty, and B. K. Mallick (2016) Fast sampling with gaussian scale mixture priors in high-dimensional regression | 0.737 | 3 | 2 | 100% |
| 9 | Li, Q., R. Xi, N. Lin, et al (2010) Bayesian regularized quantile regression | 0.737 | 3 | 2 | 100% |
| 10 | Adams, P., T. Adrian, N. Boyarchenko, and D. Giannone (2020) Forecasting macroeconomic risks | 0.644 | 2 | 2 | 100% |
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