Quantile Regression for Thinning-based INAR(1)Models of Time Series of Counts

作     者：Dan-shu SHENG De-hui WANG Kai YANG Zi-ang WU Dan-shu SHENG;De-hui WANG;Kai YANG;Zi-ang WU

作者机构：School of MathematicsJilin UniversityChangchun 130012China School of EconomicsLiaoning UniversityShenyang 110036China School of Mathematics and StatisticsChangchun University of TechnologyChangchun 130012China 

出 版 物：《Acta Mathematicae Applicatae Sinica》 (应用数学学报（英文版）)

年 卷 期：2021年第37卷第2期

页      面：264-277页

核心收录：

学科分类：02[经济学] 0202[经济学-应用经济学] 020208[经济学-统计学] 07[理学] 0714[理学-统计学（可授理学、经济学学位）] 070103[理学-概率论与数理统计] 0701[理学-数学] 

基　　金：supported by National Natural Science Foundation of China(No.11871028,11731015,12001229,11901053) Natural Science Foundation of Jilin Province(No.20180101216JC) 

主　　题：INAR(1)process quantile regression parameter estimation jittering 

摘      要：In this paper,we develop the quantile regression(QR)estimation for the first-order integer-valued autoregressive(INAR(1))models by defining the smoothing INAR(1)*** method is used to derive the QR estimators for the autoregressive coefficient and the quantile of *** consistency and asymptotic normality of the proposed estimators are *** performances of the proposed estimation procedures are evaluated by Monte Carlo *** results show that the proposed procedures perform well for simulations and a real data application.