A Unified Inference for Predictive Quantile Regression

• 成果类型: Article
• 署名作者: Liu, Xiaohui; Long, Wei; Peng, Liang; Yang, Bingduo
• 署名单位: Jiangxi University of Finance & Economics; Jiangxi University of Finance & Economics; Tulane University; University System of Georgia; Georgia State University; Guangdong University of Finance & Economics
• 刊物名称: JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION
• ISSN/ISSBN: 0162-1459
• DOI: 10.1080/01621459.2023.2203354
• 发表日期: 2024
• 页码: 1526-1540
• 关键词: robust econometric inference; STOCK RETURN PREDICTABILITY; time-series; EFFICIENT TESTS; limit theory; unit-root; stationarity; models

摘要

The asymptotic behavior of quantile regression inference becomes dramatically different when it involves a persistent predictor with zero or nonzero intercept. Distinguishing various properties of a predictor is empirically challenging. In this article, we develop a unified predictability test for quantile regression regardless of the presence of intercept and persistence of a predictor. The developed test is a novel combination of data splitting, weighted inference, and a random weighted bootstrap method. Monte Carlo simulations show that the new approach displays significantly better size and power performance than other competing methods in various scenarios, particularly when the predictive regressor contains a nonzero intercept. In an empirical application, we revisit the quantile predictability of the monthly S&P 500 returns between 1980 and 2019. for this article are available online.