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Empirical likelihood estimation of the spatial quantile regression

  • Philip Kostov

    ()

The spatial quantile regression model is a useful and flexible model for analysis of empirical problems with spatial dimension. This paper introduces an alternative estimator for this model. The properties of the proposed estimator are discussed in a comparative perspective with regard to the other available estimators. Simulation evidence on the small sample properties of the proposed estimator is provided. The proposed estimator is feasible and preferable when the model contains multiple spatial weighting matrices. Furthermore, a version of the proposed estimator based on the exponentially tilted empirical likelihood could be beneficial if model misspecification is suspect. Copyright Springer-Verlag 2013

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File URL: http://hdl.handle.net/10.1007/s10109-012-0162-3
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Article provided by Springer in its journal Journal of Geographical Systems.

Volume (Year): 15 (2013)
Issue (Month): 1 (January)
Pages: 51-69

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Handle: RePEc:kap:jgeosy:v:15:y:2013:i:1:p:51-69
Contact details of provider: Web page: http://www.springerlink.com/link.asp?id=103079

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  1. Liangjun Su & Zhenlin Yang, 2007. "Instrumental Variable Quantile Estimation of Spatial Autoregressive Models," Development Economics Working Papers 22476, East Asian Bureau of Economic Research.
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  27. Reich, Brian J. & Fuentes, Montserrat & Dunson, David B., 2011. "Bayesian Spatial Quantile Regression," Journal of the American Statistical Association, American Statistical Association, vol. 106(493), pages 6-20.
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