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International Regional Science Review
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Neural Network Modeling as a Tool for Forecasting Regional Employment Patterns

Simonetta Longhi

Department of Spatial Economics, Free University, Amsterdam, the Netherlandsslonghi{at}feweb.vu.nl

Peter Nijkamp

Department of Spatial Economics, Free University, Amsterdam, the Netherlandspnijkamp{at}feweb.vu.nl

Aura Reggianni

Department of Economics, Faculty of Statistics, University of Bologna, Bologna, Italyreggiani{at}economia.unibo.it

Erich Maierhofer

Institut fuer Arbeitsmarkt und Berufsforschung (IAB), Nuremberg, Germanyerich.maierhofer{at}iab.de

This article analyzes artificial neural networks (ANNs) as a method to compute employment forecasts at a regional level. The empirical application is based on employment data collected for 327West German regionsover a periodof fourteenyears. First, the authors compare ANNs to models commonly used in panel data analysis. Second, they verify, in the case of panel data, whether the common practice of combining forecasts of the computed models is able to produce more reliable forecasts. The technique currently employed by the German authorities to compute such regional employment forecasts is comparable to a simple naïve no-change model. For this reason, ANNs are also compared to this undemanding technique.

Key Words: regional forecasts • employment • panel data • neural networks

International Regional Science Review, Vol. 28, No. 3, 330-346 (2005)
DOI: 10.1177/0160017605276187
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S. Longhi and P. Nijkamp
Forecasting Regional Labor Market Developments under Spatial Autocorrelation
International Regional Science Review, April 1, 2007; 30(2): 100 - 119.
[Abstract] [PDF]