Neural networks for regional employment forecasts: are the parameters relevant?
AbstractIn 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 â in terms of explanatory variables and NN structures â 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.
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Bibliographic InfoArticle provided by Springer in its journal Journal of Geographical Systems.
Volume (Year): 13 (2011)
Issue (Month): 1 (March)
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Web page: http://www.springerlink.com/link.asp?id=103079
Neural networks; Sensitivity analysis; Employment forecasts; Local labour markets; C45; E27; R23;
Other versions of this item:
- Roberto Patuelli & Aura Reggiani & Peter Nijkamp & Norbert Schanne, 2009. "Neural Networks for Cross-Sectional Employment Forecasts: A Comparison of Model Specifications for Germany," Quaderni della facoltÃ di Scienze economiche dell'UniversitÃ di Lugano 0903, USI Università della Svizzera italiana.
- Roberto Patuelli & Aura Reggiani & Peter Nijkamp & Norbert Schanne, 2009. "Neural Networks for Regional Employment Forecasts: Are the Parameters Relevant?," Working Paper Series 07_09, The Rimini Centre for Economic Analysis, revised Feb 2010.
- Patuelli, R. & Reggiani, A. & Nijkamp, P., 2009. "Neural networks for cross-sectional employment forecasts: a comparison of model specifications for germany," Serie Research Memoranda 0014, VU University Amsterdam, Faculty of Economics, Business Administration and Econometrics.
- C45 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Neural Networks and Related Topics
- E27 - Macroeconomics and Monetary Economics - - Consumption, Saving, Production, Employment, and Investment - - - Forecasting and Simulation: Models and Applications
- R23 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - Household Analysis - - - Regional Migration; Regional Labor Markets; Population
Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.:
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- Roberto Patuelli & Daniel A. Griffith & Michael Tiefelsdorf & Peter Nijkamp, 2009. "Spatial Filtering and Eigenvector Stability: Space-Time Models for German Unemployment Data," Quaderni della facoltÃ di Scienze economiche dell'UniversitÃ di Lugano 0902, USI Università della Svizzera italiana.
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