Kaicheng Chen, Antonio F. Galvao, Seunghwa Rho, Timothy J. Vogelsang, Jungmo Yoon
arXiv 5 Sep 2026 · Econometrics
arXiv:2609.05883 · PDF · Extracted main text
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.
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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 | Galvao, Antonio F and Yoon, Jungmo (2024) Hac covariance matrix estimation in quantile regression self | 1.000 | 15 | 5 | 100% |
| 2 | Hwang, Jungbin and Valdés, Gonzalo (2025) HAR Inference for Quantile Regression in Time Series | 1.000 | 15 | 5 | 100% |
| 3 | Montiel Olea, José Luis and Plagborg-Møller, Mikkel (2019) Simultaneous confidence bands: Theory, implementation, and an application to SVARs | 1.000 | 6 | 3 | 100% |
| 4 | Nicholas M. Kiefer and Timothy J. Vogelsang (2005) A New Asymptotic Theory for Heteroskedasticity-Autocorrelation Robust Tests self | 1.000 | 5 | 5 | 100% |
| 5 | Zhongjun Qu (2008) Testing for structural change in regression quantiles | 0.874 | 5 | 2 | 100% |
| 6 | Yixiao Sun (2014) Let’s fix it: Fixed-b asymptotics versus small-b asymptotics in heteroskedasticity and autocorrelation robust inference | 0.874 | 5 | 2 | 100% |
| 7 | Van Der Vaart, Aad W and Wellner, Jon A (1996) Weak convergence and empirical processes: with applications to statistics | 0.874 | 5 | 2 | 100% |
| 8 | Sun, Yixiao (2011) Robust trend inference with series variance estimator and testing-optimal smoothing parameter | 0.843 | 3 | 3 | 100% |
| 9 | Su, L. and Xiao, Z (2008) Testing for parameter stability in quantile regression models | 0.811 | 4 | 2 | 100% |
| 10 | Welch, Ivo and Goyal, Amit (2008) A Comprehensive Look at The Empirical Performance of Equity Premium Prediction | 0.811 | 4 | 2 | 100% |
Showing the top 10 of 56 scored citations.