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The Use of Spatial Filtering Techniques: The Spatial and Space-time Structure of German Unemployment Data

Author

Listed:
  • Roberto Patuelli

    (Vrije Universiteit Amsterdam)

  • Daniel A. Griffith

    (University of Texas at Dallas)

  • Michael Tiefelsdorf

    (University of Texas at Dallas)

  • Peter Nijkamp

    (Vrije Universiteit Amsterdam)

Abstract

Socio-economic interrelationships among regions can be measured in terms of economic flows, migration, or physical geographically-based measures, such as distance or length of shared areal unit boundaries. In general, proximity and openness tend to favour a similar economic performance among adjacent regions. Therefore, proper forecasting of socio-economic variables, such as employment, requires an understanding of spatial (or spatio-temporal) autocorrelation effects associated with a particular geographic configuration of a system of regions. Several spatial econometric techniques have been developed in recent years to identify spatial interaction effects within a parametric framework. Alternatively, newly devised spatial filtering techniques aim to achieve this end as well through the use of a semi-parametric approach. Experiments presented in this paper deal with the analysis of and accounting for spatial autocorrelation by means of spatial filtering t! echniques for data pertaining to regional unemployment in Germany. The available data set comprises information about the share of unemployed workers in 439 German districts (the NUTS-III regional aggregation level). Results based upon an eigenvector spatial filter model formulation (that is, the use of orthogonal map pattern components), constructed for the 439 German districts, are presented, with an emphasis on their consistency over several years. Insights obtained by applying spatial filtering to the database are also discussed.

Suggested Citation

  • Roberto Patuelli & Daniel A. Griffith & Michael Tiefelsdorf & Peter Nijkamp, 2006. "The Use of Spatial Filtering Techniques: The Spatial and Space-time Structure of German Unemployment Data," Tinbergen Institute Discussion Papers 06-049/3, Tinbergen Institute.
    • Handle: RePEc:tin:wpaper:20060049
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    References listed on IDEAS

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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. Giuliano Guerra & Roberto Patuelli & Rico Maggi, 2012. "Ethnic concentration, cultural identity and immigrant self-employment in Switzerland," Chapters, in: Peter Nijkamp & Jacques Poot & Mediha Sahin (ed.), Migration Impact Assessment, chapter 4, pages 147-171, Edward Elgar Publishing.
    3. Konstantin Arkadievich Kholodilin & Boriss Siliverstovs & Stefan Kooths, 2008. "A Dynamic Panel Data Approach to the Forecasting of the GDP of German Länder," Spatial Economic Analysis, Taylor & Francis Journals, vol. 3(2), pages 195-207.
    4. 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.
    5. Nicola Pontarollo & Roberto Ricciuti, 2015. "Railways and the Productivity Gap in Italy: Persistence and Divergence after Unification," CESifo Working Paper Series 5438, CESifo.
    6. Buendía Azorín, José Daniel & Sánchez de la Vega, María del Mar, 2017. "Output growth thresholds for the creation of employment and the reduction of unemployment: A spatial analysis with panel data from the Spanish provinces, 2000–2011," Regional Science and Urban Economics, Elsevier, vol. 67(C), pages 42-49.
    7. Gloria Alarcón-García & José Daniel Buendía Azorín & María del Mar Sánchez de la Vega, 2020. "Shadow economy and national culture: A spatial approach," Hacienda Pública Española / Review of Public Economics, IEF, vol. 232(1), pages 53-74, March.
    8. Lan Hu & Yongwan Chun & Daniel A. Griffith, 2020. "Uncovering a positive and negative spatial autocorrelation mixture pattern: a spatial analysis of breast cancer incidences in Broward County, Florida, 2000–2010," Journal of Geographical Systems, Springer, vol. 22(3), pages 291-308, July.
    9. Matías Mayor & Ana Jesús López, 2009. "Spatial shift-share analysis versus spatial filtering: an application to Spanish employment data," Studies in Empirical Economics, in: Giuseppe Arbia & Badi H. Baltagi (ed.), Spatial Econometrics, pages 123-142, Springer.
    10. 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.
    11. Jesus Serrano-Lomelin & Charlene C. Nielsen & Anne Hicks & Susan Crawford & Jeffrey A. Bakal & Maria B. Ospina, 2020. "Geographic Inequalities of Respiratory Health Services Utilization during Childhood in Edmonton and Calgary, Canada: A Tale of Two Cities," IJERPH, MDPI, vol. 17(23), pages 1-17, December.
    12. Yu, Danlin & Murakami, Daisuke & Zhang, Yaojun & Wu, Xiwei & Li, Ding & Wang, Xiaoxi & Li, Guangdong, 2020. "Investigating high-speed rail construction's support to county level regional development in China: An eigenvector based spatial filtering panel data analysis," Transportation Research Part B: Methodological, Elsevier, vol. 133(C), pages 21-37.

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    More about this item

    Keywords

    spatial autocorrelation; spatial filtering; unemployment; Germany;
    All these keywords.

    JEL classification:

    • C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General
    • 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
    • R23 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - Household Analysis - - - Regional Migration; Regional Labor Markets; Population

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