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QML and Efficient GMM Estimation of Spatial Autoregressive Models with Dominant (Popular) Units

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  • Lung-Fei Lee
  • Chao Yang
  • Jihai Yu
Abstract
This article investigates QML and GMM estimation of spatial autoregressive (SAR) models in which the column sums of the spatial weights matrix might not be uniformly bounded. We develop a central limit theorem in which the number of columns with unbounded sums can be finite or infinite and the magnitude of their column sums can be O(nδ) if δ

Suggested Citation

  • Lung-Fei Lee & Chao Yang & Jihai Yu, 2023. "QML and Efficient GMM Estimation of Spatial Autoregressive Models with Dominant (Popular) Units," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 41(2), pages 550-562, April.
    • Handle: RePEc:taf:jnlbes:v:41:y:2023:i:2:p:550-562
    DOI: 10.1080/07350015.2022.2041424
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    Cited by:

    1. Badi H. Baltagi & Junjie Shu, 2024. "A Survey of Spatial Unit Roots," Mathematics, MDPI, vol. 12(7), pages 1-31, March.

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