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

Roberto Patuelli (), Daniel A. Griffith, Michael Tiefelsdorf and Peter Nijkamp
Additional contact information
Daniel A. Griffith: University of Texas at Dallas, USA
Michael Tiefelsdorf: University of Texas at Dallas, USA

Working Paper series from Rimini Centre for Economic Analysis

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.

Keywords: spatial filtering; eigenvectors; Germany; unemployment (search for similar items in EconPapers)
JEL-codes: C33 E24 R12 (search for similar items in EconPapers)
Date: 2009-01, Revised 2010-05
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Citations: View citations in EconPapers (3)

Published in International Regional Science Review, 34(2):253–280, 2011

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http://www.rcea.org/RePEc/pdf/wp02_09.pdf Revised version, May 2010
http://www.rcea.org/RePEc/pdf/wp02_09_(R_code).zip R code

Related works:
Journal Article: Spatial Filtering and Eigenvector Stability: Space-Time Models for German Unemployment Data (2011)
Working Paper: Spatial Filtering and Eigenvector Stability: Space-Time Models for German Unemployment Data (2009)
Working Paper: The Use of Spatial Filtering Techniques: The Spatial and Space-time Structure of German Unemployment Data (2006)
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Persistent link: https://EconPapers.repec.org/RePEc:rim:rimwps:02_09

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