Predictive quantile regression with mixed roots and increasing dimensions: The ALQR approach

Rui Fan, Ji Hyung Lee and Youngki Shin

Journal of Econometrics, 2023, vol. 237, issue 2

Abstract: 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.

Keywords: Adaptive lasso; Cointegration; Forecasting; Oracle property; Quantile regression (search for similar items in EconPapers)
JEL-codes: C22 C53 C61 (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (5)

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Working Paper: Predictive Quantile Regression with Mixed Roots and Increasing Dimensions: The ALQR Approach (2022)
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Persistent link: https://EconPapers.repec.org/RePEc:eee:econom:v:237:y:2023:i:2:s0304407622002111

DOI: 10.1016/j.jeconom.2022.11.006

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