标题：Covariate-Adjusted Regression for Time Series
作者：Ma, Yunyan; Luan, Yihui
作者机构：[Ma, Yunyan; Luan, Yihui] Shandong Univ, Sch Math, Jinan 250100, Shandong, Peoples R China.
通讯作者地址：[Luan, YH]Shandong Univ, Sch Math, 27 Shanda Nanlu, Jinan 250100, Shandong, Peoples R China.
来源：COMMUNICATIONS IN STATISTICS-THEORY AND METHODS
关键词：alpha-mixing; Asymptotic normality; Covariate-adjusted regression;; Functional-coefficient regression model; Goodness-of-fit test; Time; series
摘要：The main purpose of this article is to consider the covariate-adjusted regression (CAR) model for time series. The CAR model was initially proposed by Senturk and Muller (2005) for such situations where predictor and response variables are not directly observed, but are distorted by some common observable covariate. Despite CAR being originally designed for independent cross-sectional data, multiple works have extended this method to dependent data setting. In this article, the authors extend CAR to the distorted time series setting. This extension is meaningful in many fields such as econometrics, mathematical finance, and signal processing. The estimates of regression parameters are proposed by establishing connection with functional-coefficient time series model. The consistency and asymptotic normality of the proposed estimates are investigated under the alpha-mixing conditions. Real data and simulated examples are provided for illustration.