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

Author

Listed:
  • 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.

Suggested Citation

  • Roberto Patuelli & Daniel A. Griffith & Michael Tiefelsdorf & Peter Nijkamp, 2009. "Spatial Filtering and Eigenvector Stability: Space-Time Models for German Unemployment Data," Working Paper series 02_09, Rimini Centre for Economic Analysis, revised May 2010.
  • Handle: RePEc:rim:rimwps:02_09
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    References listed on IDEAS

    as
    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 369-382, October.
    2. Reinhold Kosfeld & Christian Dreger, 2006. "Thresholds for employment and unemployment: A spatial analysis of German regional labour markets, 1992-2000," Papers in Regional Science, Wiley Blackwell, vol. 85(4), pages 523-542, November.
    3. 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.
    4. Enrique López-Bazo & Tomás del Barrio & Manuel Artis, 2002. "The regional distribution of Spanish unemployment: A spatial analysis," Papers in Regional Science, Springer;Regional Science Association International, vol. 81(3), pages 365-389.
    5. Christian Bayer & Falko Jüßen, 2007. "Convergence in West German Regional Unemployment Rates," German Economic Review, Verein für Socialpolitik, vol. 8, pages 510-535, November.
    6. Maria Francesca Cracolici & Miranda Cuffaro & Peter Nijkamp, 2007. "Geographical Distribution of Unemployment: An Analysis of Provincial Differences in Italy," Growth and Change, Wiley Blackwell, vol. 38(4), pages 649-670.
    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. 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," Spatial Economic Analysis, Taylor & Francis Journals, vol. 1(1), pages 7-30.
    10. 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.
    11. Daniel Griffith & Jean Paelinck, 2009. "Specifying a joint space- and time-lag using a bivariate Poisson distribution," Journal of Geographical Systems, Springer, vol. 11(1), pages 23-36, March.
    12. Simonetta Longhi & Peter Nijkamp, 2007. "Forecasting Regional Labor Market Developments under Spatial Autocorrelation," International Regional Science Review, , vol. 30(2), pages 100-119, April.
    13. repec:dgr:uvatin:20060020 is not listed on IDEAS
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    Citations

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    Cited by:

    1. Roberto Patuelli & Norbert Schanne & Daniel A. Griffith & Peter Nijkamp, 2012. "Persistence Of Regional Unemployment: Application Of A Spatial Filtering Approach To Local Labor Markets In Germany," Journal of Regional Science, Wiley Blackwell, vol. 52(2), pages 300-323, May.
    2. 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.
    3. Giuliano Guerra & Roberto Patuelli & Rico Maggi, 2012. "Ethnic concentration, cultural identity and immigrant self-employment in Switzerland," Chapters,in: Migration Impact Assessment, chapter 4, pages 147-171 Edward Elgar Publishing.
    4. Roberto Patuelli & Andrea Vaona & Christoph Grimpe, 2010. "The German East‐West Divide In Knowledge Production: An Application To Nanomaterial Patenting," Tijdschrift voor Economische en Sociale Geografie, Royal Dutch Geographical Society KNAG, vol. 101(5), pages 568-582, December.
    5. repec:dgr:vuarem:2009-14 is not listed on IDEAS
    6. Nicola Pontarollo & Roberto Ricciuti, 2015. "Railways and the Productivity Gap in Italy: Persistence and Divergence after Unification," CESifo Working Paper Series 5438, CESifo Group Munich.
    7. Clément Gorin, 2016. "Patterns and determinants of inventors’ mobility across European urban areas," Working Papers 1615, Groupe d'Analyse et de Théorie Economique Lyon St-Étienne (GATE Lyon St-Étienne), Université de Lyon.
    8. repec:lrk:eeaart:35_2_6 is not listed on IDEAS
    9. Daisuke Murakami & Daniel Griffith, 2015. "Random effects specifications in eigenvector spatial filtering: a simulation study," Journal of Geographical Systems, Springer, vol. 17(4), pages 311-331, October.
    10. Clément Gorin, 2016. "Patterns and determinants of inventors' mobility across European urban areas," Working Papers halshs-01313086, HAL.

    More about this item

    Keywords

    spatial filtering; eigenvectors; Germany; unemployment;

    JEL classification:

    • C33 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Models with Panel Data; Spatio-temporal Models
    • E24 - Macroeconomics and Monetary Economics - - Consumption, Saving, Production, Employment, and Investment - - - Employment; Unemployment; Wages; Intergenerational Income Distribution; Aggregate Human Capital; Aggregate Labor Productivity
    • R12 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - General Regional Economics - - - Size and Spatial Distributions of Regional Economic Activity; Interregional Trade (economic geography)

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