Template-Type: ReDIF-Paper 1.0
Series: Tinbergen Institute Discussion Papers
Creation-Date: 2006-05-22
Number: 06-049/3
Author-Name: Roberto Patuelli
Author-Email: rpatuelli@feweb.vu.nl
Author-Workplace-Name: Vrije Universiteit Amsterdam
Author-Name: Daniel A. Griffith
Author-Email: dag054000@utdallas.edu
Author-Workplace-Name: University of Texas at Dallas
Author-Name: Michael Tiefelsdorf
Author-Email: tiefelsdorf@utdallas.edu
Author-Workplace-Name: University of Texas at Dallas
Author-Name: Peter Nijkamp
Author-Email: pnijkamp@feweb.vu.nl
Author-Workplace-Name: Vrije Universiteit Amsterdam
Title: The Use of Spatial Filtering Techniques: The Spatial and Space-time Structure of German Unemployment Data
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 t! echniques 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.
Classification-JEL: C14; C21; C23; R23
Keywords: spatial autocorrelation; spatial filtering; unemployment; Germany
File-Url: https://papers.tinbergen.nl/06049.pdf
File-Format: application/pdf
File-Size: 1150339 bytes
Handle: RePEc:tin:wpaper:20060049