{"title":"线性回归模型结构断点的估计","authors":"Y. Baek","doi":"10.1080/07350015.2022.2154777","DOIUrl":null,"url":null,"abstract":"This paper proposes a point estimator of the break location for a one-time structural break in linear regression models. If the break magnitude is small, the least-squares estimator of the break date has two modes at ends of the finite sample period, regardless of the true break location. I suggest a modification of the least-squares objective function to solve this problem. The modified objective function incorporates estimation uncertainty that varies across potential break dates. The new break point estimator is consistent and has a unimodal finite sample distribution under a small break magnitude. A limit distribution is provided under a in-fill asymptotic framework which verifies that the new estimator outperforms the least-squares estimator.","PeriodicalId":118766,"journal":{"name":"Journal of Business & Economic Statistics","volume":"61 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2018-11-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":"{\"title\":\"Estimation of a Structural Break Point in Linear Regression Models\",\"authors\":\"Y. Baek\",\"doi\":\"10.1080/07350015.2022.2154777\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"This paper proposes a point estimator of the break location for a one-time structural break in linear regression models. If the break magnitude is small, the least-squares estimator of the break date has two modes at ends of the finite sample period, regardless of the true break location. I suggest a modification of the least-squares objective function to solve this problem. The modified objective function incorporates estimation uncertainty that varies across potential break dates. The new break point estimator is consistent and has a unimodal finite sample distribution under a small break magnitude. A limit distribution is provided under a in-fill asymptotic framework which verifies that the new estimator outperforms the least-squares estimator.\",\"PeriodicalId\":118766,\"journal\":{\"name\":\"Journal of Business & Economic Statistics\",\"volume\":\"61 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2018-11-08\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"2\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Journal of Business & Economic Statistics\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1080/07350015.2022.2154777\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Business & Economic Statistics","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1080/07350015.2022.2154777","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Estimation of a Structural Break Point in Linear Regression Models
This paper proposes a point estimator of the break location for a one-time structural break in linear regression models. If the break magnitude is small, the least-squares estimator of the break date has two modes at ends of the finite sample period, regardless of the true break location. I suggest a modification of the least-squares objective function to solve this problem. The modified objective function incorporates estimation uncertainty that varies across potential break dates. The new break point estimator is consistent and has a unimodal finite sample distribution under a small break magnitude. A limit distribution is provided under a in-fill asymptotic framework which verifies that the new estimator outperforms the least-squares estimator.