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

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Author Info

  • Roberto Patuelli

    ()
    (University of Lugano, Switzerland; The Rimini Centre for Economic Analysis, Rimini, Italy)

  • Daniel A. Griffith

    (University of Texas at Dallas, USA)

  • Michael Tiefelsdorf

    (University of Texas at Dallas, USA)

  • Peter Nijkamp

    (VU University Amsterdam, The Netherlands)

Abstract

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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Bibliographic Info

Paper provided by The Rimini Centre for Economic Analysis in its series Working Paper Series with number 02_09.

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Date of creation: Jan 2009
Date of revision: May 2010
Publication status: Published in International Regional Science Review 34 (2): 253–280
Handle: RePEc:rim:rimwps:02_09

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Keywords: spatial filtering; eigenvectors; Germany; unemployment;

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References

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  1. Simonetta Longhi & Peter Nijkamp, 2007. "Forecasting Regional Labor Market Developments under Spatial Autocorrelation," International Regional Science Review, , vol. 30(2), pages 100-119, April.
  2. 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.
  3. Christian Bayer & Falko Juessen, 2004. "Convergence in West German Regional Unemployment Rates," Urban/Regional 0411007, EconWPA.
  4. Lambert, Dayton M. & Brown, Jason P. & Florax, Raymond J.G.M., 2010. "A two-step estimator for a spatial lag model of counts: Theory, small sample performance and an application," Regional Science and Urban Economics, Elsevier, vol. 40(4), pages 241-252, July.
  5. Maria Francesca Cracolici & Miranda Cuffaro & Peter Nijkamp, 2007. "Geographical Distribution of Unemployment: An Analysis of Provincial Differences in Italy," Growth and Change, Gatton College of Business and Economics, University of Kentucky, vol. 38(4), pages 649-670.
  6. 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.
  7. 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.
  8. 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.
  9. Roberto Patuelli & Aura Reggiani & Peter Nijkamp & Uwe Blien, 2006. "New Neural Network Methods for Forecasting Regional Employment: An Analysis of German Labour Markets," Tinbergen Institute Discussion Papers 06-020/3, Tinbergen Institute.
  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. 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.
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Citations

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Cited by:
  1. Roberto Patuelli & Aura Reggiani & Peter Nijkamp & Norbert Schanne, 2011. "Neural networks for regional employment forecasts: are the parameters relevant?," Journal of Geographical Systems, Springer, vol. 13(1), pages 67-85, March.
  2. Giuliano Guerra & Roberto Patuelli & Rico Maggi, 2010. "Ethnic Concentration, Cultural Identity and Immigrant Self-Employment in Switzerland," Quaderni della facoltà di Scienze economiche dell'Università di Lugano 1008, USI Università della Svizzera italiana.
  3. Patuelli, R. & Schanne, N. & Griffith, D.A. & Nijkamp, P., 2010. "Persistent disparities in regional unemployment: Application of a spatial filtering approach to local labour markets in Germany," Serie Research Memoranda 0001, VU University Amsterdam, Faculty of Economics, Business Administration and Econometrics.
  4. Prodromídis, Pródromos-Ioánnis K., 2012. "Modeling male and female employment policy in Greece from local data," Economic Modelling, Elsevier, vol. 29(3), pages 823-839.

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