Estimation for time-varying coefficient smoothed quantile regression

Lixia Hu, Jinhong You, Qian Huang and Shu Liu

Journal of Applied Statistics, 2025, vol. 52, issue 9, 1825-1846

Abstract: Time-varying coefficient regression is commonly used in the modeling of nonstationary stochastic processes. In this paper, we consider a time-varying coefficient convolution-type smoothed quantile regression (conquer). The covariates and errors are assumed to belong to a general class of locally stationary processes. We propose a local linear conquer estimator for the varying-coefficient function, and obtain the global Bahadur–Kiefer representation, which yields the asymptotic normality. Furthermore, statistical inference on simultaneous confidence bands is also studied. We investigate the finite-sample performance of the conquer estimator and confirm the validity of our asymptotic theory by conducting extensive simulation studies. We also consider financial volatility data as an example of a real-world application.

Date: 2025
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DOI: 10.1080/02664763.2024.2440056

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