Multicriteria Analysis of Neural Network Forecasting Models: An Application to German Regional Labour Markets
AbstractThis paper develops a flexible multi-dimensional assessment method for the comparison of different statistical-econometric techniques based on learning mechanisms, with a view to analysing and forecasting regional labour markets. The aim of this paper is twofold. A first major objective is to explore the use of a standard choice tool, namely Multicriteria Analysis (MCA), in order to cope with the intrinsic methodological uncertainty on the choice of a suitable statistical- econometric learning technique for regional labour market analysis. MCA is applied here to support choices on the performance of various models – based on classes of Neural Network (NN) techniques – that serve to generate employment forecasts in West Germany at a regional/district level. A second objective of the paper is to analyse the methodological potential of a blend of approaches (NN-MCA) in order to extend the analysis framework to other economic research domains, where formal models are not available, but where a variety of statistical data is present. The paper offers a basis for a more balanced judgement of the performance of rival statistical tests.
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Bibliographic InfoPaper provided by EconWPA in its series Experimental with number 0511001.
Length: 26 pages
Date of creation: 08 Nov 2005
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Note: Type of Document - pdf; pages: 26. Published in: Studies in Regional Science 33 (3): 205-230
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multicriteria analysis; neural networks; regional labour markets;
Find related papers by JEL classification:
- C9 - Mathematical and Quantitative Methods - - Design of Experiments
This paper has been announced in the following NEP Reports:
- NEP-ALL-2005-11-12 (All new papers)
- NEP-ECM-2005-11-12 (Econometrics)
- NEP-FOR-2005-11-12 (Forecasting)
- NEP-GEO-2005-11-12 (Economic Geography)
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