Spatial Filtering and Eigenvector Stability: Space-Time Models for German Unemployment Data
Roberto Patuelli (),
Daniel A. Griffith,
Michael Tiefelsdorf and
Peter Nijkamp
International Regional Science Review, 2011, vol. 34, issue 2, 253-280
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.
Keywords: spatial filtering; eigenvectors; Germany; unemployment; GLMM (search for similar items in EconPapers)
Date: 2011
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (14)
Downloads: (external link)
https://journals.sagepub.com/doi/10.1177/0160017610386482 (text/html)
Related works:
Working Paper: Spatial Filtering and Eigenvector Stability: Space-Time Models for German Unemployment Data (2010) 
Working Paper: Spatial Filtering and Eigenvector Stability: Space-Time Models for German Unemployment Data (2009) 
This item may be available elsewhere in EconPapers: Search for items with the same title.
Export reference: BibTeX
RIS (EndNote, ProCite, RefMan)
HTML/Text
Persistent link: https://EconPapers.repec.org/RePEc:sae:inrsre:v:34:y:2011:i:2:p:253-280
DOI: 10.1177/0160017610386482
Access Statistics for this article
More articles in International Regional Science Review
Bibliographic data for series maintained by SAGE Publications ().