文章基本信息

标题：Estimation and model selection in general spatial dynamic panel data models
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作者：Baisuo Jin ; Yuehua Wu ; Calyampudi Radhakrishna Rao 等
期刊名称：Proceedings of the National Academy of Sciences
印刷版ISSN：0027-8424
电子版ISSN：1091-6490
出版年度：2020
卷号：117
期号：10
页码：5235-5241
DOI：10.1073/pnas.1917411117
出版社：The National Academy of Sciences of the United States of America
摘要：Commonly used methods for estimating parameters of a spatial dynamic panel data model include the two-stage least squares, quasi-maximum likelihood, and generalized moments. In this paper, we present an approach that uses the eigenvalues and eigenvectors of a spatial weight matrix to directly construct consistent least-squares estimators of parameters of a general spatial dynamic panel data model. The proposed methodology is conceptually simple and efficient and can be easily implemented. We show that the proposed parameter estimators are consistent and asymptotically normally distributed under mild conditions. We demonstrate the superior performance of our approach via extensive simulation studies. We also provide a real data example.
关键词：spatial dynamic panel data model ; spatial–temporal model ; least squares ; eigendecomposition ; consistency