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Model Averaging Multistep Prediction in an Infinite Order Autoregressive Process |
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| Authors: | Yuan Huifang Lin Peng Jiang Tao Xu Jinfeng |
| Affiliation: | 1.School of Statistics and Mathematics, Zhejiang Gongshang University, Hangzhou, 310018, China ;2.School of Mathematics and Statistics, Zaozhuang University, Zaozhuang, 277000, China ;3.School of Mathematics and Statistics, Shandong University of Technology, Zibo, 255000, China ;4.Hangzhou College of Commerce, Zhejiang Gongshang University, Tonglu, Hangzhou, 311599, China ;5.Department of Statistics and Actuarial Science, The University of Hong Kong, HongKong, 999077, China ; |
| Abstract: | 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. |
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