Dynamically integrated regression model for online auction data

作     者：Mengying You Huazhen Lin Hua Liang Mengying You;Huazhen Lin;Hua Liang

作者机构：Center of Statistical ResearchSchool of StatisticsSouthwestern University of Finance and EconomicsChengdu 611130China Department of StatisticsGeorge Washington UniversityWashingtonDC 20052USA 

出 版 物：《Science China Mathematics》 (中国科学：数学（英文版）)

年 卷 期：2022年第65卷第7期

页      面：1531-1552页

核心收录：

学科分类：12[管理学] 02[经济学] 0202[经济学-应用经济学] 020208[经济学-统计学] 1201[管理学-管理科学与工程(可授管理学、工学学位)] 0701[理学-数学] 

基　　金：supported by National Natural Science Foundation of China(Grant Nos.11528102 and 11571282) Fundamental Research Funds for the Central Universities of China(Grant Nos.JBK120509 and 14TD0046) supported by the National Science Foundation of USA(Grant No.DMS-1620898)。 

主　　题：B-spline dynamic forecasting model functional linear regression model minimax rate online auction 

摘      要：We propose a dynamically integrated regression model to predict the price of online auctions,including the final price.Different from existing models,the proposed method uses not only the historical price but also the information from bidding time.Consequently,the prediction accuracy is improved compared with the existing methods.An estimation method based on B-spline approximation is proposed for the estimation and the inference of parameters and nonparametric functions in this model.The minimax rate of convergence for the prediction risk and large-sample results including the consistency and the asymptotic normality are established.Simulation studies verify the finite sample performance and the appealing prediction accuracy and robustness.Finally,when we apply our method to a 7-day auction of iPhone 6s during December 2015 and March 2016,the proposed method predicts the ending price with a much smaller error than the existing models.