Spatial Filtering Methods for Tracing Space-Time Developments in an Open Regional System: Experiments with German Unemployment Data

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. The 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 dataset comprises information about the share of unemployed workers in 439 German districts (the NUTS-III regional aggregation level). In this paper, various 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 observation years. New insights obtained by applying spatial filtering to the database about the German regional labour markets also are discussed.