HAR Inference for Quantile Regression in Time Series

Jungbin Hwang and Gonzalo Valdés
Additional contact information
Gonzalo Valdés: Universidad de Tarapacá

No 2025-03, Working papers from University of Connecticut, Department of Economics

Abstract: This paper develops robust inference for conditional quantile regression (QR) under unknown forms of weak dependence in time series data. We rst establish xed-smoothing asymptotic theory for QR by showing that the long-run variance (LRV) estimator for the non-smooth QR score process weakly converges to a random matrix scaled by the true LRV. Additionally, QR-Wald statistics based on the kernel LRV estimator converge to non-standard limits, while using orthonormal series LRV estimators yields standard F and t limits. For the practical implementation of our new asymptotic theory for Wald and t inference in QR, we extend heteroskedasticity and autocorrelation robust (HAR) inference for conditional mean regression to QR and apply the optimal smoothing parameter selection rule based on the Neyman-Pearson principle. Monte Carlo simulation results show that our QR-HAR procedure reduces size distortions of the HAR inference based on the conditional mean regression and the QR-HAC inference particularly in scenarios with moderate sample sizes, strong temporal dependence, and multiple parameters in the joint null hypothesis.

Keywords: Quantile regression; heteroskedasticity and autocorrelation robust; long-run variance; alter-native asymptotics; testing-optimal smoothing parameter choice (search for similar items in EconPapers)
JEL-codes: C12 C19 C22 C32 (search for similar items in EconPapers)
Pages: 57 pages
Date: 2025-02
New Economics Papers: this item is included in nep-ecm and nep-ets
Note: Jungbin Hwang is the corresponding author
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://media.economics.uconn.edu/working/2025-03.pdf Full text (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:uct:uconnp:2025-03

Access Statistics for this paper

More papers in Working papers from University of Connecticut, Department of Economics University of Connecticut 365 Fairfield Way, Unit 1063 Storrs, CT 06269-1063. Contact information at EDIRC.
Bibliographic data for series maintained by Mark McConnel ().