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

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  • Roberto Patuelli
  • Daniel A. Griffith
  • Michael Tiefelsdorf
  • Peter Nijkamp

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 (SAC) in geographically referenced data. The experiments carried out in this article are concerned with the analysis of the SAC observed for unemployment rates in 439 NUTS-3 German districts. The authors employ a semiparametric approach—spatial filtering—in order to uncover spatial patterns that are consistently significant over time. The authors first provide a brief overview of the spatial filtering method and illustrate the data set. Subsequently, they describe the empirical application carried out: that is, the spatial filtering analysis of regional unemployment rates in Germany. Furthermore, the authors exploit the resulting spatial filter as an explanatory variable in a panel modeling framework. Additional explanatory variables, such as average daily wages, are used in concurrence with the spatial filter. Their experiments show that the computed spatial filters account for most of the residual SAC in the data.

Suggested Citation

  • Roberto Patuelli & Daniel A. Griffith & Michael Tiefelsdorf & Peter Nijkamp, 2011. "Spatial Filtering and Eigenvector Stability: Space-Time Models for German Unemployment Data," International Regional Science Review, , vol. 34(2), pages 253-280, April.
    • Handle: RePEc:sae:inrsre:v:34:y:2011:i:2:p:253-280
    DOI: 10.1177/0160017610386482
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    Full references (including those not matched with items 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. 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: Peter Nijkamp & Jacques Poot & Mediha Sahin (ed.), Migration Impact Assessment, chapter 4, pages 147-171, Edward Elgar Publishing.
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    5. Paula Margaretic & Christine Thomas-Agnan & Romain Doucet, 2017. "Spatial dependence in (origin-destination) air passenger flows," Papers in Regional Science, Wiley Blackwell, vol. 96(2), pages 357-380, June.
    6. repec:dgr:vuarem:2009-14 is not listed on IDEAS
    7. Nicola Pontarollo & Roberto Ricciuti, 2015. "Railways and the Productivity Gap in Italy: Persistence and Divergence after Unification," CESifo Working Paper Series 5438, CESifo.
    8. Clément Gorin, 2016. "Patterns and determinants of inventors' mobility across European urban areas," Working Papers halshs-01313086, HAL.
    9. Wang, Yiyi & Kockelman, Kara M. & Wang, Xiaokun (Cara), 2013. "Understanding spatial filtering for analysis of land use-transport data," Journal of Transport Geography, Elsevier, vol. 31(C), pages 123-131.
    10. 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.
    11. 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.
    12. Buendía Azorín, José Daniel. & Sánchez De La Vega, Mª Del Mar, 2017. "Estimación del valor añadido bruto, dependencia espacial y datos de panel: Evidencia en el caso de los municipios de la Región de Murcia /Estimation of Gross Value Added, Spatial Dependence and Panel ," Estudios de Economia Aplicada, Estudios de Economia Aplicada, vol. 35, pages 315-340, Mayo.
    13. 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.
    14. 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.
    15. Hyoung Jun Kim & Bo Kyeong Lee & So Young Sohn, 2020. "Comparing spatial patterns of sole proprietorship and corporate payday lenders in Seoul, Korea," The Annals of Regional Science, Springer;Western Regional Science Association, vol. 64(1), pages 215-236, February.
    16. 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.
    17. 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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    More about this item

    Keywords

    spatial filtering; eigenvectors; Germany; unemployment; GLMM;
    All these keywords.

    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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