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  • Richard Harris
  • John Moffat
  • Victoria Kravtsova


The paper discusses the standard approaches in constructing the spatial weights matrix, W, and the implications of using such approaches in terms of the potential mis-specification of W. We then look at more recent attempts to measure W in the literature, including: Bayesian (searching for ‘best fit’); non-parametric techniques; the use of spatial correlation to estimate W; other iteration techniques; and alternative approaches. Lastly, an illustration is provided based on estimating spatial lag models determining establishment level R&D spending in the UK, finding that differently constructed W matrices produce different estimates of spatial spillovers. A la recherche du « W » R ésumé La présente communication se penche sur les méthodes standards de la structure de la matrice de poids spatiale «W», et les implications de l'emploi de ces méthodes sur le plan d'une erreur de spécification potentielle de W. Elle se penche ensuite sur des tentatives plus récentes de mesure de W dans certains ouvrages, y compris des inférences bayésiennes (recherche de meilleure probabilité); des techniques non paramétriques; l'emploi d'une corrélation spatiale pour l’évaluation de W; des techniques d'itération diverses; et d'autres méthodes en alternative. Enfin, elle contient une illustration basée sur l'estimation de modèles à décalage spatial permettant de déterminer le niveau d’établissement de dépenses en R&D au Royaume-Uni, qui conclut que des matrices W à structure différente produisent différentes évaluations de débordement spatial. En busca de ‘ W ’ E xtracto Este trabajo trata los planteamientos típicos al construir la matriz de ponderaciones espaciales, W, y las implicaciones de utilizar dichos planteamientos en términos de la posible especificación errónea de W. Seguidamente, examinamos intentos más recientes de ponderar W en la bibliografía, incluyendo: bayesiano (búsqueda de lo que ‘mejor encaja’); técnicas no paramétricas; el uso de correlación espacial para estimar W; otras técnicas de iteración; y planteamientos alternativos. Finalmente, se ofrece una ilustración basada en estimar modelos de retardo (lag) espacial que determinan el gasto en I+D a nivel de establecimiento en el Reino Unido; se descubre que matrices W construidas de forma diferente producen estimaciones diferentes de excedentes (spillovers) espaciales.

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Bibliographic Info

Article provided by Taylor & Francis Journals in its journal Spatial Economic Analysis.

Volume (Year): 6 (2011)
Issue (Month): 3 (February)
Pages: 249-270

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Handle: RePEc:taf:specan:v:6:y:2011:i:3:p:249-270

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Cited by:
  1. Ernest Miguélez & Rosina Moreno, 2013. "Do Labour Mobility and Technological Collaborations Foster Geographical Knowledge Diffusion? The Case of European Regions," Growth and Change, Gatton College of Business and Economics, University of Kentucky, vol. 44(2), pages 321-354, 06.
  2. R. Basile & S. Usai, 2012. "Analysis of regional endogenous growth," Working Paper CRENoS 201211, Centre for North South Economic Research, University of Cagliari and Sassari, Sardinia.
  3. Ernest Miguele & Rosina Moreno, 2012. "Do labour mobility and networks foster geographical knowledge diffusion? The case of European regions," Working Papers XREAP2012-14, Xarxa de Referència en Economia Aplicada (XREAP), revised Jul 2012.


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