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Instrumental Variable Quantile Estimation of Spatial Autoregressive Models

Zhenlin Yang () and Liangjun Su

No 05-2007, Working Papers from Singapore Management University, School of Economics

Abstract: We propose an instrumental variable quantile regression (IVQR) estimator for spatial autoregressive (SAR) models. Like the GMM estimators of Lin and Lee (2006) and Kelejian and Prucha (2006), the IVQR estimator is robust against heteroscedasticity. Unlike the GMM estimators, the IVQR estimator is also robust against outliers and requires weaker moment conditions. More importantly, it allows us to characterize the heterogeneous impact of variables on different points (quantiles) of a response distribution. We derive the limiting distribution of the new estimator. Simulation results show that the new estimator performs well in finite samples at various quantile points. In the special case of median restriction, it outperforms the conventional QML estimator without taking into account of heteroscedasticity in the errors; it also outperforms the GMM estimators with or without considering the heteroscedasticity.

Keywords: Spatial Autoregressive Model; Quantile Regression; Instrumental Variable; Quasi Maximum Likelihood; GMM; Robustness. (search for similar items in EconPapers)
JEL-codes: C13 C21 C51 (search for similar items in EconPapers)
Pages: 35 pages
Date: 2007-08
New Economics Papers: this item is included in nep-ecm, nep-geo, nep-sea and nep-ure
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Published in SMU Economics and Statistics Working Paper Series

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