Neural Network Modeling as a Tool for Forecasting Regional Employment Patterns

Simonetta Longhi, Peter Nijkamp, Aura Reggianni and Erich Maierhofer
Additional contact information
Aura Reggianni: Department of Economics, Faculty of Statistics, University of Bologna, Bologna, Italyreggiani@economia.unibo.it
Erich Maierhofer: Institut fuer Arbeitsmarkt und Berufsforschung (IAB), Nuremberg, Germanyerich.maierhofer@iab.de

International Regional Science Review, 2005, vol. 28, issue 3, 330-346

Abstract: 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.

Keywords: regional forecasts; employment; panel data; neural networks (search for similar items in EconPapers)
Date: 2005
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (13)

Downloads: (external link)
https://journals.sagepub.com/doi/10.1177/0160017605276187 (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:sae:inrsre:v:28:y:2005:i:3:p:330-346

DOI: 10.1177/0160017605276187

Access Statistics for this article

More articles in International Regional Science Review
Bibliographic data for series maintained by SAGE Publications ().