Modeling Spatial Autocorrelation in Spatial Interaction Data: A Comparison of Spatial Econometric and Spatial Filtering Specifications
AbstractThe 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.
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Bibliographic InfoPaper provided by European Regional Science Association in its series ERSA conference papers with number ersa06p10.
Date of creation: Aug 2006
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This paper has been announced in the following NEP Reports:
- NEP-ALL-2007-01-13 (All new papers)
- NEP-ECM-2007-01-13 (Econometrics)
- NEP-GEO-2007-01-13 (Economic Geography)
- NEP-URE-2007-01-13 (Urban & Real Estate Economics)
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- PEETERS, Dominique & THOMAS, Isabelle, . "Network autocorrelation," CORE Discussion Papers RP -2168, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
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