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The Spatial Durbin Model and the Common Factor Tests

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  • Jesús Mur
  • Ana Angulo

Abstract

Abstract The spatial Durbin model occupies an interesting position in the field of spatial econometrics. It is the reduced form of a model with cross-sectional dependence in the errors and it may be used as the nesting equation in a more general approach of model selection. Specifically, in this equation we obtain the common factor tests (of which the likelihood ratio is the best known) whose objective is to discriminate between substantive and residual dependence in an apparently misspecified equation. Our paper tries to delve deeper into the role of the spatial Durbin model in the problem of specifying a spatial econometric model. We include a Monte Carlo study related to the performance of the common factor tests presented in the paper in small sample sizes.

Suggested Citation

  • Jesús Mur & Ana Angulo, 2006. "The Spatial Durbin Model and the Common Factor Tests," Spatial Economic Analysis, Taylor & Francis Journals, vol. 1(2), pages 207-226.
  • Handle: RePEc:taf:specan:v:1:y:2006:i:2:p:207-226 DOI: 10.1080/17421770601009841
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    References listed on IDEAS

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    Cited by:

    1. Macfarlane, Gregory S. & Garrow, Laurie A. & Moreno-Cruz, Juan, 2015. "Do Atlanta residents value MARTA? Selecting an autoregressive model to recover willingness to pay," Transportation Research Part A: Policy and Practice, Elsevier, vol. 78(C), pages 214-230.
    2. Catherine BAUMONT & Rachel GUILLAIN, 2013. "Interactions, Spillovers De Connaissance Et Croissance Des Villes Européennes - Quel Est Le Rôle De La Géographie, Du Climat Institutionnel Et Des Réseaux Des Firmes Multinationales ?," Region et Developpement, Region et Developpement, LEAD, Universite du Sud - Toulon Var, pages 161-207.
    3. Angulo, Ana M. & Mur, Jesús, 2011. "The Likelihood Ratio Test of Common Factors under Non-Ideal Conditions," INVESTIGACIONES REGIONALES - Journal of REGIONAL RESEARCH, Asociación Española de Ciencia Regional, issue 21, pages 37-52.
    4. Arbia, Giuseppe & Battisti, Michele & Di Vaio, Gianfranco, 2010. "Institutions and geography: Empirical test of spatial growth models for European regions," Economic Modelling, Elsevier, vol. 27(1), pages 12-21, January.
    5. Takagi, Daisuke & Ikeda, Ken’ichi & Kawachi, Ichiro, 2012. "Neighborhood social capital and crime victimization: Comparison of spatial regression analysis and hierarchical regression analysis," Social Science & Medicine, Elsevier, pages 1895-1902.
    6. Jesús Mur & Fernando López & Ana Angulo, 2010. "Instability in spatial error models: an application to the hypothesis of convergence in the European case," Journal of Geographical Systems, Springer, vol. 12(3), pages 259-280, September.
    7. Burridge, Peter, 2011. "A research agenda on general-to-specific spatial model search," INVESTIGACIONES REGIONALES - Journal of REGIONAL RESEARCH, Asociación Española de Ciencia Regional, issue 21, pages 71-90.
    8. Timo Mitze, 2010. "Network Dependency in Migration Flows – A Space-time Analysis for Germany since Re-unification," Ruhr Economic Papers 0205, Rheinisch-Westfälisches Institut für Wirtschaftsforschung, Ruhr-Universität Bochum, Universität Dortmund, Universität Duisburg-Essen.
    9. Masha Maslianskaia-Pautrel & Catherine Baumont pba148, 2016. "The nature and impacts of environmental spillovers on housing prices: A spatial hedonic analysis," Working Papers 2016.04, FAERE - French Association of Environmental and Resource Economists.
    10. repec:zbw:rwirep:0205 is not listed on IDEAS
    11. Carlo Ciccarelli & Jean Paul Elhorst, 2016. "A Spatial Diffusion Model with Common Factors and an Application to Cigarette Consumption," CEIS Research Paper 381, Tor Vergata University, CEIS, revised 31 May 2016.
    12. Mitze, Timo, 2010. "Network Dependency in Migration Flows – A Space-time Analysis for Germany since Re-unification," Ruhr Economic Papers 205, RWI - Leibniz-Institut für Wirtschaftsforschung, Ruhr-University Bochum, TU Dortmund University, University of Duisburg-Essen.
    13. Nicola Pontarollo, 2013. "Structural change, productivity growth and Structural Funds in European regions," ERSA conference papers ersa13p747, European Regional Science Association.

    More about this item

    Keywords

    Common factor tests; spatial lag model; spatial error model; C21; C50; R15;

    JEL classification:

    • C21 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models
    • C50 - Mathematical and Quantitative Methods - - Econometric Modeling - - - General
    • R15 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - General Regional Economics - - - Econometric and Input-Output Models; Other Methods

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