Multicriteria Analysis of Neural Network Forecasting Models: An Application to German Regional Labour Markets
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概要
- 論文の詳細を見る
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.
- 日本地域学会の論文
著者
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NIJKAMP Peter
Department of Spatial Economics, Vrije University Amsterdam
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PATUELLI Roberto
School of Public Policy, George Mason University
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LONGHI Simonetta
Tinbergen Institute of Amsterdam
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REGGIANI Aura
Department of Economics, Faculty of Statistics, University of Bologna
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NIJKAMP Peter
Department of Spatial Economics, Free University
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NIJKAMP PETER
Department of Regional Economics, Free University
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