报告主题：Statistical Inference for Time-varying Coefficient Smoothed Quantile Regression
时间：2023年4月28日13:30-14:30
地点：上川路校区五教117/腾讯会议 会议号：984 1292 8754 密码：6666
报告人：胡黎霞
报告内容简介：A time-varying coefficient regression is commonly used in the modeling of nonstationary stochastic processes. This paper considers 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. Furthermore, we also show the asymptotic distribution and obtain pointwise as well as simultaneous confidence bands for the varying-coefficient function. Extensive simulation studies investigate the finite-sample performance of the conquer estimator and confirm the validity of our asymptotic theory. A financial volatility data is also considered as an illustration in the real-world applications.