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A closer look at the Spatial Durbin Model

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  • Jesus Mur

    ()

  • Ana Angulo

    ()

Abstract

The spatial Durbin model occupies an interesting position in Spatial Econometrics. It is the reduced form of a model with cross-sectional dependence in the errors, but it may be used, also, as the nesting model in a more general approach of model selection. In the first case, that is the equation where we solve the Likelihood Ratio test of Common Factors. The objective in this case is to discriminate between substantive and residual dependence in a misspecified equation. Its role, when discussing the specification of the model, is also of great value as a way to access either to a static model, to a dynamic model or to a model with residual dependence. Our paper tries to go further into the interpretation of this intermediate equation in both aspects. We include a small Monte Carlo study related to the LR tests and present some new results that expedites the use, and the interpretation, of the Durbin equation in the more general process of econometric model selection in a spatial context.

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Paper provided by European Regional Science Association in its series ERSA conference papers with number ersa05p392.

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Date of creation: Aug 2005
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Handle: RePEc:wiw:wiwrsa:ersa05p392

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  1. Florax, Raymond J. G. M. & Folmer, Hendrik & Rey, Sergio J., 2003. "Specification searches in spatial econometrics: the relevance of Hendry's methodology," Regional Science and Urban Economics, Elsevier, Elsevier, vol. 33(5), pages 557-579, September.
  2. Bernard Fingleton & Enrique López-Bazo, 2006. "Empirical growth models with spatial effects," Papers in Regional Science, Wiley Blackwell, Wiley Blackwell, vol. 85(2), pages 177-198, 06.
  3. Robin Dubin, 2003. "Robustness of Spatial Autocorrelation Specifications: Some Monte Carlo Evidence," Journal of Regional Science, Wiley Blackwell, Wiley Blackwell, vol. 43(2), pages 221-248.
  4. Blommestein, Hans J., 1983. "Specification and estimation of spatial econometric models : A discussion of alternative strategies for spatial economic modelling," Regional Science and Urban Economics, Elsevier, Elsevier, vol. 13(2), pages 251-270, May.
  5. P Burridge, 1981. "Testing for a common factor in a spatial autoregression model," Environment and Planning A, Pion Ltd, London, Pion Ltd, London, vol. 13(7), pages 795-800, July.
  6. Jesús Mur & Ana Angulo, 2005. "Model selection strategies in a spatial context," Documentos de Trabajo, Facultad de Ciencias Económicas y Empresariales, Universidad de Zaragoza dt2005-06, Facultad de Ciencias Económicas y Empresariales, Universidad de Zaragoza.
  7. Anselin, Luc & Bera, Anil K. & Florax, Raymond & Yoon, Mann J., 1996. "Simple diagnostic tests for spatial dependence," Regional Science and Urban Economics, Elsevier, Elsevier, vol. 26(1), pages 77-104, February.
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