Multicriteria Analysis of Neural Network Forecasting Models: An Application to German Regional Labour Markets
Roberto Patuelli (),
Simonetta Longhi (),
Aura Reggiani () and
Experimental from EconWPA
This 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.
Keywords: multicriteria analysis; neural networks; regional labour markets (search for similar items in EconPapers)
JEL-codes: C9 (search for similar items in EconPapers)
New Economics Papers: this item is included in nep-ecm, nep-for and nep-geo
Note: Type of Document - pdf; pages: 26. Published in: Studies in Regional Science 33 (3): 205-230
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Persistent link: http://EconPapers.repec.org/RePEc:wpa:wuwpex:0511001
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