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Modeling Spatial Autocorrelation in Spatial Interaction Data: A Comparison of Spatial Econometric and Spatial Filtering Specifications

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  • Manfred M. Fischer
  • Daniel A. Griffith

Abstract

The need to account for spatial autocorrelation is well known in spatial analysis. Many spatial statistics and spatial econometric texts detail the way spatial autocorrelation can be identified and modelled in the case of object and field data. The literature on spatial autocorrelation is much less developed in the case of spatial interaction data. The focus of interest in this paper is on the problem of spatial autocorrelation in a spatial interaction context. The paper aims to illustrate that eigenfunction-based spatial filtering offers a powerful methodology that can efficiently account for spatial autocorrelation effects within a Poisson spatial interaction model context that serves the purpose to identify and measure spatial separation effects to interregional knowledge spillovers as captured by patent citations among high-technology-firms in Europe.

Suggested Citation

  • Manfred M. Fischer & Daniel A. Griffith, 2006. "Modeling Spatial Autocorrelation in Spatial Interaction Data: A Comparison of Spatial Econometric and Spatial Filtering Specifications," ERSA conference papers ersa06p10, European Regional Science Association.
    • Handle: RePEc:wiw:wiwrsa:ersa06p10
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    References listed on IDEAS

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    1. repec:cor:louvrp:-2168 is not listed on IDEAS
    2. PEETERS, Dominique & THOMAS, Isabelle, 2009. "Network autocorrelation," LIDAM Reprints CORE 2168, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
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    Cited by:

    1. Daniel A. Griffith & Manfred M. Fischer, 2016. "Constrained Variants of the Gravity Model and Spatial Dependence: Model Specification and Estimation Issues," Advances in Spatial Science, in: Roberto Patuelli & Giuseppe Arbia (ed.), Spatial Econometric Interaction Modelling, chapter 0, pages 37-66, Springer.

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