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Higher-order least squares inference for spatial autoregressions

Rossi, Francesca and Robinson, Peter M. (2022) Higher-order least squares inference for spatial autoregressions. Journal of Econometrics. ISSN 0304-4076

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Identification Number: 10.1016/j.jeconom.2022.01.010

Abstract

We develop refined inference for spatial regression models with predetermined regressors. The ordinary least squares estimate of the spatial parameter is neither consistent nor asymptotically normal, unless the elements of the spatial weight matrix uniformly vanish as sample size diverges. We develop refined testing of the hypothesis of no spatial dependence, without requiring such negligibility of spatial weights, by formal Edgeworth expansions. We also develop such higher-order expansions for both an unstudentized and a studentized transformed estimate, where the studentized one can be used to provide refined interval estimates. A Monte Carlo study of finite sample performance is included.

Item Type: Article
Official URL: https://www.sciencedirect.com/journal/journal-of-e...
Additional Information: © 2022 Elsevier B.V.
Divisions: Economics
Subjects: H Social Sciences > HB Economic Theory
JEL classification: C - Mathematical and Quantitative Methods > C1 - Econometric and Statistical Methods: General > C12 - Hypothesis Testing
C - Mathematical and Quantitative Methods > C1 - Econometric and Statistical Methods: General > C13 - Estimation
C - Mathematical and Quantitative Methods > C2 - Econometric Methods: Single Equation Models; Single Variables > C21 - Cross-Sectional Models; Spatial Models; Treatment Effect Models
Date Deposited: 13 Apr 2022 14:42
Last Modified: 08 Jun 2022 10:06
URI: http://eprints.lse.ac.uk/id/eprint/114885

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