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Estimation Strategies for a Spatial Dynamic Panel using GMM. A New Approach to the Convergence Issue of European Regions


Author Info

  • Salima Bouayad-Agha
  • Lionel V�drine


Abstract While estimation methods for dynamic panel data and spatial econometric models are standard in economic literature, there has been a relatively recent development in methods which include spatial considerations in dynamic panel data models. This paper proposes two estimation strategies for spatial dynamic panel data models using the generalized method of moments (GMM). The first is to extend the moment restrictions of Arellano and Bond's estimator to a spatial autoregressive dynamic panel. The second allows for spatial dependence in the error process. The empirical application focuses on European regional growth over a 25-year period. We find empirical evidence of conditional convergence, which is significantly affected by spatial disparities. Stratégies d'estimation pour un panel dynamique spatial faisant usage de GMM. Une nouvelle approche pour le problème de la convergence de régions d'Europe Rèsumè Bien que les méthodes d'estimation pour les données de panels dynamiques, et les modèles économétriques spatiaux, sont des instruments standards dans les ouvrages d’économie, on a assisté à une évolution relativement récente des méthodes, qui comprend des considérations spatiales dans les modèles de panels dynamiques. La présente communication propose deux stratégies d'estimation concernant des modèles de données de panel dynamique spatiales faisant usage de la méthodes des moments généralisés (MMG). La première consiste à étendre les restrictions de moments de l'estimateur d'Arellano et Bond à un panel dynamique autorégressif spatial. La deuxième tient compte de la dépendance spatiale dans le processus des erreurs. L'application empirique se concentre sur l'expansion régionale en Europe au cours d'une période de 25 ans. Nous relevons des preuves empiriques de convergence conditionnelle, qui sont affectées de façon significative par des disparités spatiales. Estrategias de estimación para un panel dinámico espacial utilizando GMM. Un nuevo planteamiento de la cuestión de la convergencia de regiones europeas Extracto Aunque los métodos de estimación para datos dinámicos de panel y modelos econométricos espaciales son estándar en la bibliografía económica, se ha producido un desarrollo relativamente reciente en dichos métodos que incluye consideraciones espaciales en modelos de datos dinámicos de panel. Este estudio propone dos estrategias de estimación para los modelos de datos espaciales dinámicos de panel utilizando el método general de momentos (GMM). El primero sirve para extender las restricciones de momentos del estimador de Arellano y Bond a un panel espacial dinámico autorregresivo. El segundo tiene en cuenta una dependencia espacial en el proceso de error. La aplicación empírica se centra en el crecimiento regional europeo en un período de 25 años. Descubrimos evidencia empírica de convergencia condicional, que es afectada significativamente por disparidades espaciales.

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Bibliographic Info

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

Volume (Year): 5 (2010)
Issue (Month): 2 ()
Pages: 205-227

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Handle: RePEc:taf:specan:v:5:y:2010:i:2:p:205-227

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Keywords: Spatial econometrics; dynamic panel model; GMM; regional convergence; C21; C23; O52; R11;

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Cited by:
  1. Timo Mitze, 2010. "Within and Between Panel Cointegration in the German Regional Output-Trade-FDI Nexus," Ruhr Economic Papers 0222, Rheinisch-Westfälisches Institut für Wirtschaftsforschung, Ruhr-Universität Bochum, Universität Dortmund, Universität Duisburg-Essen.
  2. Roberto Patuelli & Daniel A. Griffith & Michael Tiefelsdorf & Peter Nijkamp, 2011. "Spatial Filtering and Eigenvector Stability: Space-Time Models for German Unemployment Data," International Regional Science Review, , vol. 34(2), pages 253-280, April.
  3. Lena Tonzer, 2013. "Cross-Border Interbank Networks, Banking Risk and Contagion," FIW Working Paper series 129, FIW.
  4. 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.
  5. Kubis, Alexander & Schneider, Lutz, 2012. "Human capital mobility and convergence : a spatial dynamic panel model of the German regions," IAB Discussion Paper 201223, Institut für Arbeitsmarkt- und Berufsforschung (IAB), Nürnberg [Institute for Employment Research, Nuremberg, Germany].
  6. Hermann Pythagore Pierre Donfouet & P. Wilner Jeanty & Eric Malin, 2013. "A Spatial Dynamic Panel Analysis of Corruption," Economics Working Paper Archive (University of Rennes 1 & University of Caen) 201324, Center for Research in Economics and Management (CREM), University of Rennes 1, University of Caen and CNRS.
  7. Giulio Cainelli & Sandro Montresor & Giuseppe Vittucci Marzetti, 2014. "Spatial agglomeration and firm exit: a spatial dynamic analysis for Italian provinces," Small Business Economics, Springer, vol. 43(1), pages 213-228, June.
  8. Vogel, Johanna, 2013. "Regional Convergence in Europe: A Dynamic Heterogeneous Panel Approach," MPRA Paper 51794, University Library of Munich, Germany.
  9. Hermann Pythagore Pierre Donfouet & P. Wilner Jeanty & Eric Malin, 2013. "A Spatial Dynamic Panel Analysis of the Environmental Kuznets Curve in European Countries," Economics Working Paper Archive (University of Rennes 1 & University of Caen) 201318, Center for Research in Economics and Management (CREM), University of Rennes 1, University of Caen and CNRS.
  10. Badi H. Baltagi & Bernard Fingleton & Alain Pirotte, 2012. "Estimating and Forecasting With A Dynamic Spatial Panel Data Model," Center for Policy Research Working Papers 149, Center for Policy Research, Maxwell School, Syracuse University.
  11. Salima Bouayad-Agha & Nadine Turpin & Lionel Védrine, 2010. "Fostering the potential endogenous development of European regions: a spatial dynamic panel data analysis of the Cohesion Policy on regional convergence over the period 1980-2005," TEPP Working Paper 2010-17, TEPP.
  12. Massimo Filippini & Laura González & Giuliano Masiero, 2010. "Estimating dynamic consumption of antibiotics using panel data: the shadow effect of bacterial resistance," Quaderni della facoltà di Scienze economiche dell'Università di Lugano 1011, USI Università della Svizzera italiana.


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