Modified QML Estimation of Spatial Autoregressive Models with Unknown Heteroskedasticity and Nonnormality

Shew Fan Liu () and Zhenlin Yang
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Shew Fan Liu: School of Economics, Singapore Management University, Singapore, 178903

No 14-2014, Working Papers from Singapore Management University, School of Economics

Abstract: In the presence of heteroskedasticity, Lin and Lee (2010) show that the quasi maximum likelihood (QML) estimators of spatial autoregressive models (SAR) 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 many practical situations and when it does the regular QML estimators can be consistent. In cases where this condition is violated, we propose a modified QML estimation method robust against heteroskedasticity of unknown form. 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 estimators, and the regular QML estimator even when it is consistent. The proposed robust inference methods can also be easily applied.

Keywords: Spatial dependence; Unknown heteroskedasticity; Nonnormality; Modified QML estimator; Robust standard error (search for similar items in EconPapers)
JEL-codes: C10 C13 C15 C21 (search for similar items in EconPapers)
Pages: 36 pages
Date: 2014-09
New Economics Papers: this item is included in nep-ecm, nep-ore and nep-sea
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Published in SMU Economics and Statistics Working Paper Series

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Journal Article: Modified QML estimation of spatial autoregressive models with unknown heteroskedasticity and nonnormality (2015)
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