{"title":"不假设平均平稳性的识别:具有内生回归因子的动态面板模型的准极大似然估计","authors":"H. Kruiniger","doi":"10.1093/ectj/utaa036","DOIUrl":null,"url":null,"abstract":"\n Linear generalised method of moments (GMM) estimators for dynamic panel models with predetermined or endogenous regressors suffer from a weak instruments problem when the data are highly persistent. In this paper, we propose new random- and fixed-effects limited-information quasi–maximum likelihood estimators (LIQMLEs) for such models. We also discuss LIQMLEs for models that contain time-varying individual effects. Unlike system GMM estimators, the LIQMLEs do not require mean stationarity conditions for consistency. Such conditions often do not hold for the models we consider. Our LIQMLEs are based on a two-step control function approach that includes the first-stage model residuals for a predetermined or endogenous regressor in the outcome equation. The LIMLEs are more precise than nonlinear GMM estimators that are based on the original outcome equation. The LIQMLEs also compare favourably to various alternative (quasi–) maximum likelihood estimators in terms of precision, robustness, and/or ease of computation.","PeriodicalId":50555,"journal":{"name":"Econometrics Journal","volume":null,"pages":null},"PeriodicalIF":2.9000,"publicationDate":"2020-12-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":"{\"title\":\"Identification without assuming mean stationarity: quasi–maximum likelihood estimation of dynamic panel models with endogenous regressors\",\"authors\":\"H. Kruiniger\",\"doi\":\"10.1093/ectj/utaa036\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"\\n Linear generalised method of moments (GMM) estimators for dynamic panel models with predetermined or endogenous regressors suffer from a weak instruments problem when the data are highly persistent. In this paper, we propose new random- and fixed-effects limited-information quasi–maximum likelihood estimators (LIQMLEs) for such models. We also discuss LIQMLEs for models that contain time-varying individual effects. Unlike system GMM estimators, the LIQMLEs do not require mean stationarity conditions for consistency. Such conditions often do not hold for the models we consider. Our LIQMLEs are based on a two-step control function approach that includes the first-stage model residuals for a predetermined or endogenous regressor in the outcome equation. The LIMLEs are more precise than nonlinear GMM estimators that are based on the original outcome equation. The LIQMLEs also compare favourably to various alternative (quasi–) maximum likelihood estimators in terms of precision, robustness, and/or ease of computation.\",\"PeriodicalId\":50555,\"journal\":{\"name\":\"Econometrics Journal\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":2.9000,\"publicationDate\":\"2020-12-10\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"2\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Econometrics Journal\",\"FirstCategoryId\":\"96\",\"ListUrlMain\":\"https://doi.org/10.1093/ectj/utaa036\",\"RegionNum\":4,\"RegionCategory\":\"经济学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"ECONOMICS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Econometrics Journal","FirstCategoryId":"96","ListUrlMain":"https://doi.org/10.1093/ectj/utaa036","RegionNum":4,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"ECONOMICS","Score":null,"Total":0}
Identification without assuming mean stationarity: quasi–maximum likelihood estimation of dynamic panel models with endogenous regressors
Linear generalised method of moments (GMM) estimators for dynamic panel models with predetermined or endogenous regressors suffer from a weak instruments problem when the data are highly persistent. In this paper, we propose new random- and fixed-effects limited-information quasi–maximum likelihood estimators (LIQMLEs) for such models. We also discuss LIQMLEs for models that contain time-varying individual effects. Unlike system GMM estimators, the LIQMLEs do not require mean stationarity conditions for consistency. Such conditions often do not hold for the models we consider. Our LIQMLEs are based on a two-step control function approach that includes the first-stage model residuals for a predetermined or endogenous regressor in the outcome equation. The LIMLEs are more precise than nonlinear GMM estimators that are based on the original outcome equation. The LIQMLEs also compare favourably to various alternative (quasi–) maximum likelihood estimators in terms of precision, robustness, and/or ease of computation.
期刊介绍:
The Econometrics Journal was established in 1998 by the Royal Economic Society with the aim of creating a top international field journal for the publication of econometric research with a standard of intellectual rigour and academic standing similar to those of the pre-existing top field journals in econometrics. The Econometrics Journal is committed to publishing first-class papers in macro-, micro- and financial econometrics. It is a general journal for econometric research open to all areas of econometrics, whether applied, computational, methodological or theoretical contributions.