Neural Networks for Regional Employment Forecasts: Are the Parameters Relevant?
| Autor: | Peter Nijkamp, Aura Reggiani, Norbert Schanne, Roberto Patuelli |
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| Přispěvatelé: | R. Patuelli, A. Reggiani, P. Nijkamp, N. Schanne, Spatial Economics, CLUE+ |
| Jazyk: | angličtina |
| Rok vydání: | 2011 |
| Předmět: |
Structure (mathematical logic)
Economics and Econometrics Employment forecast Artificial neural network Series (mathematics) business.industry Geography Planning and Development Local labour markets NEURAL NETWORKS computer.software_genre Power (physics) Geography Specification SENSITIVITY ANALYSIS Sensitivity (control systems) Artificial intelligence Data mining business computer |
| Zdroj: | Journal of Geographical Systems, 13(1), 67-85. Springer Verlag Patuelli, R, Reggiani, A, Nijkamp, P & Schanne, N 2011, ' Neural networks for regional employment forecasts: Are the parameters relevant ', Journal of Geographical Systems, vol. 13, no. 1, pp. 67-85 . https://doi.org/10.1007/s10109-010-0133-5 |
| ISSN: | 1435-5930 |
| DOI: | 10.1007/s10109-010-0133-5 |
| Popis: | In this paper, we present a review of various computational experiments concerning neural network (NN) models developed for regional employment forecasting. NNs are nowadays widely used in several fields because of their flexible specification structure. A series of NN experiments is presented in the paper, using two data sets on German NUTS-3 districts. Individual forecasts are computed by our models for each district in order to answer the following question: How relevant are NN parameters in comparison to NN structure? Comprehensive testing of these parameters is limited in the literature. Building on different specifications of NN models-in terms of explanatory variables and NN structures-we propose a systematic choice of NN learning parameters and internal functions by means of a sensitivity analysis. Our results show that different combinations of NN parameters provide significantly varying statistical performance and forecasting power. Finally, we note that the sets of parameters chosen for a given model specification cannot be light-heartedly applied to different or more complex models. © 2010 Springer-Verlag. |
| Databáze: | OpenAIRE |
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