Model averaging prediction for time series models with a diverging number of parameters

doi:10.1016/j.jeconom.2020.10.004

CSpace

	Model averaging prediction for time series models with a diverging number of parameters
	Liao, Jun 1,2; Zou, Guohua 2; Gao, Yan 3; Zhang, Xinyu 4
	2021-07-01
发表期刊	JOURNAL OF ECONOMETRICS
ISSN	0304-4076
卷号	223 期号:1 页码:190-221
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.
关键词	Asymptotic optimality Autoregressive process Consistency Mallows criterion Model averaging
DOI	10.1016/j.jeconom.2020.10.004
收录类别	SCI
语种	英语
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
WOS类目	Economics ; Mathematics, Interdisciplinary Applications ; Social Sciences, Mathematical Methods
WOS记录号	WOS:000646253400008
出版者	ELSEVIER SCIENCE SA
引用统计
文献类型	期刊论文
条目标识符	http://ir.amss.ac.cn/handle/2S8OKBNM/58591
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.Renmin Univ China, Sch Stat, Beijing 100872, Peoples R China 2.Capital Normal Univ, Sch Math Sci, Beijing 100048, Peoples R China 3.Minzu Univ China, Coll Sci, Beijing 100081, Peoples R China 4.Chinese Acad Sci, Acad Math & Syst Sci, Beijing 100190, 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.