典型文献
Model Averaging Multistep Prediction in an Infinite Order Autoregressive Process
文献摘要：
The key issue in the frequentist model averaging is the choice of weights.In this paper,the authors advocate an asymptotic framework of mean-squared prediction error(MSPE)and develop a model averaging criterion for multistep prediction in an infinite order autoregressive(AR(∞))process.Under the assumption that the order of the candidate model is bounded,this criterion is proved to be asymptotically optimal,in the sense of achieving the lowest out of sample MSPE for the same-realization prediction.Simulations and real data analysis further demonstrate the effectiveness and the efficiency of the theoretical results.
文献关键词：
中图分类号：
作者姓名：
YUAN Huifang;LIN Peng;JIANG Tao;XU Jinfeng
作者机构：
School of Statistics and Mathematics,Zhejiang Gongshang University,Hangzhou 310018,China; School of Mathematics and Statistics,Zaozhuang University,Zaozhuang 277000,China;School of Mathematics and Statistics,Shandong University of Technology,Zibo 255000,China;Hangzhou College of Commerce,Zhejiang Gongshang University,Tonglu 311599,China; School of Statistics and Mathematics,Zhejiang Gongshang University,Hangzhou 310018,China;Department of Statistics and Actuarial Science,The University of Hong Kong,HongKong 999077,China
文献出处：
引用格式：
[1]YUAN Huifang;LIN Peng;JIANG Tao;XU Jinfeng-.Model Averaging Multistep Prediction in an Infinite Order Autoregressive Process)[J].系统科学与复杂性学报（英文版）,2022(05):1875-1901
A类：
Multistep
B类：
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AB值：
0.65412
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