{"title":"通过交叉验证确定高维因子模型中的中断数","authors":"Ruichao Zhou, Lu Wang, Jianhong Wu","doi":"10.1515/snde-2022-0037","DOIUrl":null,"url":null,"abstract":"Abstract This paper proposes a cross-validation method to estimate the number of breaks in high-dimensional factor models. To preserve the original change structure, the parity-splitting strategy is adopted when employing the cross-validation method. The consistency of the estimator is established under some mild conditions. Simulation results show desired finite sample performance of the proposed method, especially in comparison with methods that need to predetermine the tuning parameters.","PeriodicalId":46709,"journal":{"name":"Studies in Nonlinear Dynamics and Econometrics","volume":"310 8","pages":"0"},"PeriodicalIF":0.7000,"publicationDate":"2023-11-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Determination of the Number of Breaks in High-Dimensional Factor Models via Cross-Validation\",\"authors\":\"Ruichao Zhou, Lu Wang, Jianhong Wu\",\"doi\":\"10.1515/snde-2022-0037\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Abstract This paper proposes a cross-validation method to estimate the number of breaks in high-dimensional factor models. To preserve the original change structure, the parity-splitting strategy is adopted when employing the cross-validation method. The consistency of the estimator is established under some mild conditions. Simulation results show desired finite sample performance of the proposed method, especially in comparison with methods that need to predetermine the tuning parameters.\",\"PeriodicalId\":46709,\"journal\":{\"name\":\"Studies in Nonlinear Dynamics and Econometrics\",\"volume\":\"310 8\",\"pages\":\"0\"},\"PeriodicalIF\":0.7000,\"publicationDate\":\"2023-11-07\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Studies in Nonlinear Dynamics and Econometrics\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1515/snde-2022-0037\",\"RegionNum\":4,\"RegionCategory\":\"经济学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q3\",\"JCRName\":\"ECONOMICS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Studies in Nonlinear Dynamics and Econometrics","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1515/snde-2022-0037","RegionNum":4,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"ECONOMICS","Score":null,"Total":0}
Determination of the Number of Breaks in High-Dimensional Factor Models via Cross-Validation
Abstract This paper proposes a cross-validation method to estimate the number of breaks in high-dimensional factor models. To preserve the original change structure, the parity-splitting strategy is adopted when employing the cross-validation method. The consistency of the estimator is established under some mild conditions. Simulation results show desired finite sample performance of the proposed method, especially in comparison with methods that need to predetermine the tuning parameters.
期刊介绍:
Studies in Nonlinear Dynamics & Econometrics (SNDE) recognizes that advances in statistics and dynamical systems theory may increase our understanding of economic and financial markets. The journal seeks both theoretical and applied papers that characterize and motivate nonlinear phenomena. Researchers are required to assist replication of empirical results by providing copies of data and programs online. Algorithms and rapid communications are also published.