Roberto Patuelli () (Institute for Economic Research (IRE), University of Lugano, Switzerland; The Rimini Centre for Economic Analysis (RCEA), Italy) Daniel A. Griffith () (School of Economic, Political and Policy Sciences, University of Texas at Dallas, USA) Michael Tiefelsdorf () (School of Economic, Political and Policy Sciences, University of Texas at Dallas, USA) Peter Nijkamp () (Department of Spatial Economics, VU University Amsterdam, The Netherlands)
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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.
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