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Finite Sample Properties of Estimators of Spatial Models with Autoregressive, or Moving Average, Disturbances and System Feedback

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  • Bernard Fingleton
  • Julie Le Gallo

Abstract

This paper extends Kelejian and Prucha's [1998] feasible generalized spatial two-stage least squares (FGS2SLS) estimator to account for endogenous variables due to system feedback, given an autoregressive or a moving average error process. An empirical example illustrating the different estimators is proposed. The finite sample properties of the estimators are investigated by means of Monte-Carlo simulations depending of the sample size, the weights matrix, the presence of cross-equation correlation and the nature of the instruments.

Suggested Citation

  • 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.
  • Handle: RePEc:adr:anecst:y:2007:i:87-88:p:39-62
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    Cited by:

    1. Giuseppe Arbia & Bernard Fingleton, 2008. "New spatial econometric techniques and applications in regional science," Papers in Regional Science, Wiley Blackwell, vol. 87(3), pages 311-317, August.
    2. 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.
    3. Seya, Hajime & Nakamichi, Kumiko & Yamagata, Yoshiki, 2016. "The residential parking rent price elasticity of car ownership in Japan," Transportation Research Part A: Policy and Practice, Elsevier, vol. 85(C), pages 123-134.
    4. Shi, Miaoying & Yin, Runsheng & Lv, Hongdi, 2017. "An empirical analysis of the driving forces of forest cover change in northeast China," Forest Policy and Economics, Elsevier, vol. 78(C), pages 78-87.

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