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Model averaging for interval-valued data
Sun, Yuying1,2,3; Zhang, Xinyu1,2,4; Wan, Alan T. K.5; Wang, Shouyang1,2,3
2022-09-01
Source PublicationEUROPEAN JOURNAL OF OPERATIONAL RESEARCH
ISSN0377-2217
Volume301Issue:2Pages:772-784
AbstractIn recent years, model averaging, by which estimates are obtained based on not one single model but a weighted ensemble of models, has received growing attention as an alternative to model selection. To date, methods for model averaging have been developed almost exclusively for point-valued data, despite the fact that interval-valued data are commonplace in many applications and the substantial body of literature on estimation and inference methods for interval-valued data. This paper focuses on the special case of interval time series data, and assumes that the mid-point and log-range of the interval values are modelled by a two-equation vector autoregressive with exogenous covariates (VARX) model. We develop a methodology for combining models of varying lag orders based on a weight choice criterion that minimises an unbiased estimator of the squared error risk of the model average estimator. We prove that this method yields predictors of mid-points and ranges with an optimal asymptotic property. In addition, we develop a method for correcting the range forecasts, taking into account the forecast error variance. An extensive simulation experiment examines the performance of the proposed model averaging method in finite samples. We apply the method to an interval-valued data series on crude oil future prices.(c) 2021 Published by Elsevier B.V.
KeywordForecasting Asymptotic optimality Interval-valued time series Model averaging Vector autoregression
DOI10.1016/j.ejor.2021.11.015
Indexed BySCI
Language英语
Funding ProjectNational Natural Science Foundation of China (NNSFC)[72073126] ; National Natural Science Foundation of China (NNSFC)[72091212] ; National Natural Science Foundation of China (NNSFC)[71925007] ; National Natural Science Foundation of China (NNSFC)[71631008] ; National Natural Science Foundation of China (NNSFC)[11688101] ; National Natural Science Foundation of China (NNSFC)[71973116] ; National Natural Science Foundation of China (NNSFC)[71988101] ; Fundamental Research Funds for the Central Universities in UIBE[19YB26] ; Hong Kong Research Grants Council[CityU 11500419] ; National Key R&D Program of China[2020AAA0105200] ; Academy for Multidisciplinary Studies, Capital Normal University
WOS Research AreaBusiness & Economics ; Operations Research & Management Science
WOS SubjectManagement ; Operations Research & Management Science
WOS IDWOS:000793723100026
PublisherELSEVIER
Citation statistics
Document Type期刊论文
Identifierhttp://ir.amss.ac.cn/handle/2S8OKBNM/61322
Collection中国科学院数学与系统科学研究院
Corresponding AuthorZhang, Xinyu
Affiliation1.Chinese Acad Sci, Acad Math & Syst Sci, Beijing, Peoples R China
2.Chinese Acad Sci, Ctr Forecasting Sci, Beijing, Peoples R China
3.Univ Chinese Acad Sci, Sch Econ & Management, Beijing, Peoples R China
4.Beijing Acad Artificial Intelligence, Beijing, Peoples R China
5.City Univ Hong Kong, Dept Management Sci, Hong Kong, Peoples R China
Recommended Citation
GB/T 7714
Sun, Yuying,Zhang, Xinyu,Wan, Alan T. K.,et al. Model averaging for interval-valued data[J]. EUROPEAN JOURNAL OF OPERATIONAL RESEARCH,2022,301(2):772-784.
APA Sun, Yuying,Zhang, Xinyu,Wan, Alan T. K.,&Wang, Shouyang.(2022).Model averaging for interval-valued data.EUROPEAN JOURNAL OF OPERATIONAL RESEARCH,301(2),772-784.
MLA Sun, Yuying,et al."Model averaging for interval-valued data".EUROPEAN JOURNAL OF OPERATIONAL RESEARCH 301.2(2022):772-784.
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