期刊
PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA
卷 117, 期 10, 页码 5235-5241出版社
NATL ACAD SCIENCES
DOI: 10.1073/pnas.1917411117
关键词
spatial dynamic panel data model; spatial-temporal model; least squares; eigendecomposition; consistency
资金
- National Natural Science Foundation [71873128, 11571337, 71631006, 71921001]
- Natural Sciences and Engineering Research Council of Canada [RGPIN-2017-05720]
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.
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