发表机构
Shanghai University of Finance and Economics; Michigan State University; Hanyang University, Korea（上海财经大学; 密歇根州立大学; 韩国汉阳大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文提出时间序列分位数回归的固定平滑推断方法，实现跨分位数均匀有效，通过均匀-τ和堆叠Wald两种方法解决非渐近枢轴问题，模拟显示优于HAC方法，实证揭示股票收益预测效应的异质性。
AI 中文摘要
本文针对时间序列分位数回归开发了固定平滑（fixed-b, fixed-K）推断方法，该方法对异方差性和自相关具有稳健性。我们的方法在分位数水平上均匀有效，并同时考虑了时间维度和分位数维度上的依赖性。它能够构建均匀置信带、用于联合假设的Wald检验和Sup-t检验，以及形状约束检验，为评估分位数效应的异质性提供了一个统一框架。一个关键挑战是，在弱依赖条件下，分位数回归过程的均匀推断通常是非渐近枢轴的，因为极限分布依赖于跨分位数的长期协方差结构。为解决这一问题，我们开发了两种互补的方法。均匀-τ方法估计协方差结构并模拟非渐近枢轴的极限分布。对于涉及有限分位数水平集合的某些检验，堆叠Wald方法提供了渐近枢轴的固定平滑推断。我们建立了这两种方法的渐近有效性。模拟结果表明，与现有的基于HAC的程序相比，所提出的方法在保持良好功效的同时显著改善了水平控制。一个应用于股票收益预测性分位数回归的实例显示，预测效应在分位数和预测期上均存在显著的异质性。
英文摘要
This paper develops fixed-smoothing (fixed-b, fixed-K) inference methods for time-series quantile regression that are robust to heteroskedasticity and autocorrelation. Our approach is uniformly valid over quantile levels and accounts for dependence both over time and across quantiles. It enables the construction of uniform confidence bands, Wald, and Sup-t tests for joint hypotheses, and tests of shape restrictions, providing a unified framework for assessing heterogeneity in quantile effects. A key challenge is that, under weak dependence, uniform inference for quantile regression processes is generally non-pivotal because the limiting distributions depend on the long-run covariance structure across quantiles. To address this issue, we develop two complementary approaches. The uniform-in-$τ$ method estimates the covariance structure and simulates the non-pivotal limiting distribution. For certain tests involving a finite collection of quantile levels, the stack-Wald method delivers pivotal fixed-smoothing inference. We establish the asymptotic validity of both approaches. Simulation results show that the proposed methods substantially improve size control relative to existing HAC-based procedures while maintaining good power. An application to predictive quantile regressions for stock returns reveals substantial heterogeneity in predictive effects across both quantiles and forecast horizons.
