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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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    1. Bernard Fingleton & Julie Le Gallo, 2007. "Finite Sample Properties of Estimators of Spatial Models with Autoregressive, or Moving Average, Disturbances and System Feedback," Annals of Economics and Statistics, GENES, issue 87-88, pages 39-62.
    2. Kelejian, Harry H & Prucha, Ingmar R, 1999. "A Generalized Moments Estimator for the Autoregressive Parameter in a Spatial Model," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 40(2), pages 509-533, May.
    3. Kelejian, Harry H. & Prucha, Ingmar R., 2004. "Estimation of simultaneous systems of spatially interrelated cross sectional equations," Journal of Econometrics, Elsevier, vol. 118(1-2), pages 27-50.
    4. Jonathan Temple, 1999. "The New Growth Evidence," Journal of Economic Literature, American Economic Association, vol. 37(1), pages 112-156, March.
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    6. Bernard Fingleton, 2003. "Increasing returns: evidence from local wage rates in Great Britain," Oxford Economic Papers, Oxford University Press, vol. 55(4), pages 716-739, October.
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    10. Kelejian, Harry H & Prucha, Ingmar R, 1998. "A Generalized Spatial Two-Stage Least Squares Procedure for Estimating a Spatial Autoregressive Model with Autoregressive Disturbances," The Journal of Real Estate Finance and Economics, Springer, vol. 17(1), pages 99-121, July.
    11. Julie Le Gallo & Luc Anselin, 2006. "Interpolation of air quality measures in hedonic house price models: spatial aspects," Post-Print hal-00401242, HAL.
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    14. Kelejian, Harry H. & Prucha, Ingmar R., 2007. "HAC estimation in a spatial framework," Journal of Econometrics, Elsevier, vol. 140(1), pages 131-154, September.
    15. Bernard Fingleton, 2008. "A generalized method of moments estimator for a spatial model with moving average errors, with application to real estate prices," Empirical Economics, Springer, vol. 34(1), pages 35-57, February.
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