Model averaging prediction for time series models with a diverging number of parameters
文献类型：期刊论文
| 作者 | Liao, Jun3,4; Zou, Guohua3; Gao, Yan2; Zhang, Xinyu1 |
| 刊名 | JOURNAL OF ECONOMETRICS
 |
| 出版日期 | 2021-07-01 |
| 卷号 | 223期号:1页码:190-221 |
| 关键词 | Asymptotic optimality Autoregressive process Consistency Mallows criterion Model averaging |
| ISSN号 | 0304-4076 |
| DOI | 10.1016/j.jeconom.2020.10.004 |
| 英文摘要 | An important problem with the model averaging approach is the choice of weights. In this paper, a generalized Mallows model averaging (GMMA) criterion for choosing weights is developed in the context of an infinite order autoregressive (AR(infinity)) process. The GMMA method adapts to the circumstances in which the dimensions of candidate models can be large and increase with the sample size. The GMMA method is shown to be asymptotically optimal in the sense of achieving the lowest out-of-sample mean squared prediction error (MSPE) for both the independent-realization and the same-realization predictions, which, as a byproduct, solves a conjecture put forward by Hansen (2008) that the well-known Mallows model averaging criterion from Hansen (2007) is asymptotically optimal for predicting the future of a time series. The rate of the GMMA-based weight estimator tending to the optimal weight vector minimizing the independent-realization MSPE is derived as well. Both simulation experiment and real data analysis illustrate the merits of the GMMA method in the prediction of an AR(infinity) process. (C) 2020 Elsevier B.V. All rights reserved. |
| 资助项目 | National Natural Science Foundation of China[12001534] ; National Natural Science Foundation of China[11971323] ; National Natural Science Foundation of China[12031016] ; National Natural Science Foundation of China[71925007] ; National Natural Science Foundation of China[71522004] ; National Natural Science Foundation of China[71631008] ; Ministry of Science and Technology of China[2016YFB0502301] ; National Key R&D Program of China[2020AAA0105200] ; Beijing Academy of Artificial Intelligence, China ; Youth Innovation Promotion Association of the Chinese Academy of Sciences |
| WOS研究方向 | Business & Economics ; Mathematics ; Mathematical Methods In Social Sciences |
| 语种 | 英语 |
| WOS记录号 | WOS:000646253400008 |
| 出版者 | ELSEVIER SCIENCE SA |
| 源URL | [http://ir.amss.ac.cn/handle/2S8OKBNM/58590]  |
| 专题 | 中国科学院数学与系统科学研究院 |
| 通讯作者 | Zou, Guohua |
| 作者单位 | 1.Chinese Acad Sci, Acad Math & Syst Sci, Beijing 100190, Peoples R China 2.Minzu Univ China, Coll Sci, Beijing 100081, Peoples R China 3.Capital Normal Univ, Sch Math Sci, Beijing 100048, Peoples R China 4.Renmin Univ China, Sch Stat, Beijing 100872, Peoples R China |
| 推荐引用方式 GB/T 7714 | Liao, Jun,Zou, Guohua,Gao, Yan,et al. Model averaging prediction for time series models with a diverging number of parameters[J]. JOURNAL OF ECONOMETRICS,2021,223(1):190-221. |
| APA | Liao, Jun,Zou, Guohua,Gao, Yan,&Zhang, Xinyu.(2021).Model averaging prediction for time series models with a diverging number of parameters.JOURNAL OF ECONOMETRICS,223(1),190-221. |
| MLA | Liao, Jun,et al."Model averaging prediction for time series models with a diverging number of parameters".JOURNAL OF ECONOMETRICS 223.1(2021):190-221. |
入库方式： OAI收割
来源：数学与系统科学研究院
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