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Estimating spatial models with endogenous variables, a spatial lag and spatially dependent disturbances: Finite sample properties

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  • Bernard Fingleton
  • Julie Le Gallo

Abstract

. This paper discusses estimation methods for models including an endogenous spatial lag, additional endogenous variables due to system feedback and an autoregressive or a moving average error process. It extends Kelejian and Prucha's, and Fingleton and Le Gallo's feasible generalized spatial two‐stage least squares estimators and also considers HAC estimation in a spatial framework as suggested by Kelejian and Prucha. An empirical example using real estate data illustrating the different estimators is proposed. The finite sample properties of the estimators are finally investigated by means of Monte Carlo simulation. Resumen. Este artículo discute métodos de estimación para modelos incluyendo un intervalo espacial endógeno, variables endógenas adicionales debido a retroalimentación del sistema y un proceso autorregresivo o uno de error de media móvil. Amplia Kelejian y Prucha's, y los estimadores de mínimos cuadrados bietápicos espaciales generalizados factibles de Fingleton y Le Gallo y considera también la estimación HAC en un marco espacial tal y como sugieren Kelejian y Prucha. Proponemos un ejemplo empírico utilizando datos de bienes inmuebles ilustrando los diferentes estimadores. Las propiedades en muestras finitas de los estimadores se estudian finalmente mediante simulación de Monte Carlo.

Suggested Citation

  • Bernard Fingleton & Julie Le Gallo, 2008. "Estimating spatial models with endogenous variables, a spatial lag and spatially dependent disturbances: Finite sample properties," Papers in Regional Science, Wiley Blackwell, vol. 87(3), pages 319-339, August.
    • Handle: RePEc:bla:presci:v:87:y:2008:i:3:p:319-339
    DOI: 10.1111/j.1435-5957.2008.00187.x
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