Publication Type

Journal Article

Publication Date

5-2015

Abstract

In the presence of heteroskedasticity, Lin and Lee (2010) show that the quasi-maximum likelihood (QML) estimator of the spatial autoregressive (SAR) model can be inconsistent as a ‘necessary’ condition for consistency can be violated, and thus propose robust GMM estimators for the model. In this paper, we first show that this condition may hold in certain situations and when it does the regular QML estimator can still be consistent. In cases where this condition is violated, we propose a simple modified QML estimation method robust against unknown heteroskedasticity. In both cases, asymptotic distributions of the estimators are derived, and methods for estimating robust variances are given, leading to robust inferences for the model. Extensive Monte Carlo results show that the modified QML estimator outperforms the GMM and QML estimators even when the latter is consistent. The proposed methods are then extended to the more general SARAR models.

Keywords

Spatial dependence, Unknown heteroskedasticity, Nonnormality, Modified QML estimator, Robust standard error, SARAR models

Discipline

Econometrics | Economics

Research Areas

Econometrics

Publication

Regional Science and Urban Economics

Volume

52

First Page

50

Last Page

70

ISSN

0166-0462

Identifier

10.1016/j.regsciurbeco.2015.02.003

Publisher

Elsevier

Copyright Owner and License

Authors

Creative Commons License

Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License
This work is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License.

Additional URL

http://dx.doi.org/10.1016/j.regsciurbeco.2015.02.003

Included in

Econometrics Commons

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