Quantile regression for dynamic partially linear varying coefficient time series models

In this article, we consider quantile regression method for partially linear varying coefficient models for semiparametric time series modeling. We propose estimation methods based on general series estimation. We establish convergence rates of the estimator and the root-n asymptotic normality of the finite-dimensional parameter in the linear part. We further propose penalization-based method for automatically specifying the linear part of the model as well as performing variable selection, and show the model selection consistency of this approach. We illustrate the performance of estimates using a simulation study.