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Spatial Filtering and Eigenvector Stability: Space-Time Models for German Unemployment Data

  • Roberto Patuelli

    ()

    (Institute for Economic Research (IRE), University of Lugano, Switzerland; The Rimini Centre for Economic Analysis (RCEA), Italy)

  • Daniel A. Griffith

    ()

    (School of Economic, Political and Policy Sciences, University of Texas at Dallas, USA)

  • Michael Tiefelsdorf

    ()

    (School of Economic, Political and Policy Sciences, University of Texas at Dallas, USA)

  • Peter Nijkamp

    ()

    (Department of Spatial Economics, VU University Amsterdam, The Netherlands)

Regions, independent of their geographic level of aggregation, are known to be interrelated partly due to their relative locations. Similar economic performance among regions can be attributed to proximity. Consequently, a proper understanding, and accounting, of spatial liaisons is needed in order to effectively forecast regional economic variables. Several spatial econometric techniques are available in the literature, which deal with the spatial autocorrelation in geographically-referenced data. The experiments carried out in this paper are concerned with the analysis of the spatial autocorrelation observed for unemployment rates in 439 NUTS-3 German districts. We employ a semi-parametric approach – spatial filtering – in order to uncover spatial patterns that are consistently significant over time. We first provide a brief overview of the spatial filtering method and illustrate the data set. Subsequently, we describe the empirical application carried out: that is, the spatial filtering analysis of regional unemployment rates in Germany. Furthermore, we exploit the resulting spatial filter as an explanatory variable in a panel modelling framework. Additional explanatory variables, such as average daily wages, are used in concurrence with the spatial filter. Our experiments show that the computed spatial filters account for most of the residual spatial autocorrelation in the data.

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Paper provided by USI Università della Svizzera italiana in its series Quaderni della facoltà di Scienze economiche dell'Università di Lugano with number 0902.

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Length: 22 pages
Date of creation: Jan 2009
Date of revision:
Handle: RePEc:lug:wpaper:0902
Contact details of provider: Web page: https://www.bul.sbu.usi.ch

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  1. Weinhold, Diana, 2002. "The Importance of Trade and Geography in the Pattern of Spatial Dependence of Growth Rates," Review of Development Economics, Wiley Blackwell, vol. 6(3), pages 369-82, October.
  2. Maria Francesca Cracolici & Miranda Cuffaro & Peter Nijkamp, 2007. "Geographical Distribution of Unemployment: An Analysis of Provincial Differences in Italy," Tinbergen Institute Discussion Papers 07-065/3, Tinbergen Institute.
  3. Reinhold Kosfeld & Christian Dreger, 2005. "Thresholds for Employment and Unemployment - a Spatial Analysis of German Regional Labour Markets 1992-2000," ERSA conference papers ersa05p39, European Regional Science Association.
  4. Falko Juessen & Christian Bayer, 2005. "Convergence in West German Regional Unemployment Rates," ERSA conference papers ersa05p410, European Regional Science Association.
  5. Dayton M. Lambert & Jason P. Brown & Raymond J.G.M. Florax, 2010. "A Two-Step Estimator For A Spatial Lag Model Of Counts: Theory, Small Sample Performance And An Application," Working Papers 10-5, Purdue University, College of Agriculture, Department of Agricultural Economics.
  6. Olivier Jean Blanchard & Lawrence F. Katz, 1992. "Regional Evolutions," Brookings Papers on Economic Activity, Economic Studies Program, The Brookings Institution, vol. 23(1), pages 1-76.
  7. Anselin, Luc, 2002. "Under the hood : Issues in the specification and interpretation of spatial regression models," Agricultural Economics, Blackwell, vol. 27(3), pages 247-267, November.
  8. Simonetta Longhi & Peter Nijkamp, 2007. "Forecasting Regional Labor Market Developments under Spatial Autocorrelation," International Regional Science Review, , vol. 30(2), pages 100-119, April.
  9. Roberto Patuelli & Aura Reggiani & Peter Nijkamp & Uwe Blien, 2006. "New Neural Network Methods for Forecasting Regional Employment: an Analysis of German Labour Markets," Spatial Economic Analysis, Taylor & Francis Journals, vol. 1(1), pages 7-30.
  10. Enrique López-Bazo & Tomás del Barrio & Manuel Artis, 2002. "The regional distribution of Spanish unemployment: A spatial analysis," Papers in Regional Science, Springer, vol. 81(3), pages 365-389.
  11. Salima Bouayad-Agha & Lionel V�drine, 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.
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