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