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

Tinbergen Institute Discussion Paper No. 06-049/3

24 Pages Posted: 8 Jun 2006

See all articles by Roberto Patuelli

Roberto Patuelli

University of Bologna - Department of Economics

Daniel A. Griffith

University of Texas at Dallas - School of Economic, Political and Policy Sciences

Michael Tiefelsdorf

University of Texas at Dallas

Peter Nijkamp

VU University of Amsterdam - Department of Spatial Economics; Tinbergen Institute

Date Written: May 2006

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 techniques 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.

Keywords: spatial autocorrelation, spatial filtering, unemployment, Germany

JEL Classification: C14, C21, C23, R23

Suggested Citation

Patuelli, Roberto and Griffith, Daniel A. and Tiefelsdorf, Michael and Nijkamp, Peter, The Use of Spatial Filtering Techniques: The Spatial and Space-Time Structure of German Unemployment Data (May 2006). Tinbergen Institute Discussion Paper No. 06-049/3, Available at SSRN: https://ssrn.com/abstract=893540 or http://dx.doi.org/10.2139/ssrn.893540

Roberto Patuelli (Contact Author)

University of Bologna - Department of Economics ( email )

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Daniel A. Griffith

University of Texas at Dallas - School of Economic, Political and Policy Sciences ( email )

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Michael Tiefelsdorf

University of Texas at Dallas ( email )

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Peter Nijkamp

VU University of Amsterdam - Department of Spatial Economics ( email )

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