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

Author

Listed:
  • Salima Bouayad-Agha
  • Lionel Védrine

Abstract

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.

Suggested Citation

  • Salima Bouayad-Agha & Lionel Védrine, 2010. "Estimation Strategies for a Spatial Dynamic Panel using GMM. A New Approach to the Convergence Issue of European Regions," Spatial Economic Analysis, Taylor & Francis Journals, vol. 5(2), pages 205-227.
  • Handle: RePEc:taf:specan:v:5:y:2010:i:2:p:205-227
    DOI: 10.1080/17421771003730711
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    Citations

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    Cited by:

    1. S. 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," Working Papers halshs-00812077, HAL.
    2. Vogel, Johanna, 2013. "Regional Convergence in Europe: A Dynamic Heterogeneous Panel Approach," MPRA Paper 51794, University Library of Munich, Germany.
    3. 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.
    4. Badi H. Baltagi & Bernard Fingleton & Alain Pirotte, 2014. "Estimating and Forecasting with a Dynamic Spatial Panel Data Model," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 76(1), pages 112-138, February.
    5. 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.
    6. Breidenbach, Philipp & Mitze, Timo & Schmidt, Christoph M., 2016. "EU structural funds and regional income convergence: A sobering experience," Ruhr Economic Papers 608, RWI - Leibniz-Institut für Wirtschaftsforschung, Ruhr-University Bochum, TU Dortmund University, University of Duisburg-Essen.
    7. Tonzer, Lena, 2015. "Cross-border interbank networks, banking risk and contagion," Journal of Financial Stability, Elsevier, vol. 18(C), pages 19-32.
    8. 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.
    9. Mitze, Timo, 2011. "Within and Between Panel Cointegration in the German Regional Output-Trade-FDI Nexus," INVESTIGACIONES REGIONALES - Journal of REGIONAL RESEARCH, Asociación Española de Ciencia Regional, issue 21, pages 93-118.
    10. Roberto Patuelli & Daniel A. Griffith & Michael Tiefelsdorf & Peter Nijkamp, 2009. "Spatial Filtering and Eigenvector Stability: Space-Time Models for German Unemployment Data," Quaderni della facoltà di Scienze economiche dell'Università di Lugano 0902, USI Università della Svizzera italiana.
    11. 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].
    12. 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.
    13. Luisa Corrado & Bernard Fingleton, 2016. "The W Matrix in Network and Spatial Econometrics: Issues Relating to Specification and Estimation," CEIS Research Paper 369, Tor Vergata University, CEIS, revised 12 Feb 2016.
    14. repec:zbw:rwirep:0222 is not listed on IDEAS
    15. 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.
    16. repec:zbw:rwirep:0205 is not listed on IDEAS
    17. Jonathan Eberle & Thomas Brenner, 2016. "More bucks, more growth, more justice? The effects of regional structural funds on regional economic growth and convergence in Germany," Working Papers on Innovation and Space 2016-01, Philipps University Marburg, Department of Geography.
    18. 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.
    19. 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.

    More about this item

    Keywords

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

    JEL classification:

    • C21 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models
    • C23 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Models with Panel Data; Spatio-temporal Models
    • O52 - Economic Development, Innovation, Technological Change, and Growth - - Economywide Country Studies - - - Europe
    • R11 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - General Regional Economics - - - Regional Economic Activity: Growth, Development, Environmental Issues, and Changes

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