Rui Fan, Ji Hyung Lee, Youngki Shin
arXiv 27 Jan 2021 · Econometrics · publishedJournal of Econometrics (2023) · 5 citations (OpenAlex)
arXiv:2101.11568 · PDF · DOI · OpenAlex · Extracted main text
In this paper we propose the adaptive lasso for predictive quantile regression (ALQR). Reflecting empirical findings, we allow predictors to have various degrees of persistence and exhibit different signal strengths. The number of predictors is allowed to grow with the sample size. We study regularity conditions under which stationary, local unit root, and cointegrated predictors are present simultaneously. We next show the convergence rates, model selection consistency, and asymptotic distributions of ALQR. We apply the proposed method to the out-of-sample quantile prediction problem of stock returns and find that it outperforms the existing alternatives. We also provide numerical evidence from additional Monte Carlo experiments, supporting the theoretical results.
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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 | Zou, H (2006) The adaptive lasso and its oracle properties | 0.928 | 4 | 3 | 100% |
| 2 | Lu, X. and L. Su (2015) Jackknife model averaging for quantile regressions | 0.874 | 6 | 4 | 67% |
| 3 | Koenker, R (2005) Quantile Regression | 0.737 | 3 | 3 | 67% |
| 4 | Fan, R. and J. H. Lee (2019) Predictive quantile regressions under persistence and conditional heteroskedasticity self | 0.737 | 3 | 2 | 100% |
| 5 | Koo, B., H. M. Anderson, M. H. Seo, and W. Yao (2020) High-dimensional predictive regression in the presence of cointegration | 0.737 | 3 | 2 | 100% |
| 6 | Lee, J. H., Z. Shi, and Z. Gao (2021) On lasso for predictive regression self | 0.737 | 3 | 2 | 100% |
| 7 | Zheng, Q., L. Peng, and X. He (2015) Globally adaptive quantile regression with ultra-high dimensional data | 0.737 | 3 | 2 | 100% |
| 8 | Welch, I. and A. Goyal (2008) A comprehensive look at the empirical performance of equity premium prediction | 0.693 | 5 | 1 | 100% |
| 9 | Withers, C (1981) Conditions for linear processes to be strong-mixing | 0.644 | 4 | 2 | 50% |
| 10 | Ruppert, D. and R. J. Carroll (1980) Trimmed least squares estimation in the linear model | 0.644 | 3 | 2 | 67% |
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