{"title":"看似不相关回归模型的实证似然比检验","authors":"Chuan-hua Wei, Xiaoxiao Ma","doi":"10.5539/IJSP.V10N3P1","DOIUrl":null,"url":null,"abstract":"This paper considers the problem of testing independence of equations in a seemingly unrelated regression model. A\nnovel empirical likelihood test approach is proposed, and under the null hypothesis it is shown to follow asymptotically a\nchi-square distribution. Finally, simulation studies and a real data example are conducted to illustrate the performance of\nthe proposed method.","PeriodicalId":89781,"journal":{"name":"International journal of statistics and probability","volume":"10 1","pages":"1"},"PeriodicalIF":0.0000,"publicationDate":"2021-03-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Empirical Likelihood Ratio Test for Seemingly Unrelated Regression Models\",\"authors\":\"Chuan-hua Wei, Xiaoxiao Ma\",\"doi\":\"10.5539/IJSP.V10N3P1\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"This paper considers the problem of testing independence of equations in a seemingly unrelated regression model. A\\nnovel empirical likelihood test approach is proposed, and under the null hypothesis it is shown to follow asymptotically a\\nchi-square distribution. Finally, simulation studies and a real data example are conducted to illustrate the performance of\\nthe proposed method.\",\"PeriodicalId\":89781,\"journal\":{\"name\":\"International journal of statistics and probability\",\"volume\":\"10 1\",\"pages\":\"1\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2021-03-04\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"International journal of statistics and probability\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.5539/IJSP.V10N3P1\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"International journal of statistics and probability","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.5539/IJSP.V10N3P1","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Empirical Likelihood Ratio Test for Seemingly Unrelated Regression Models
This paper considers the problem of testing independence of equations in a seemingly unrelated regression model. A
novel empirical likelihood test approach is proposed, and under the null hypothesis it is shown to follow asymptotically a
chi-square distribution. Finally, simulation studies and a real data example are conducted to illustrate the performance of
the proposed method.