Neural networks for regional employment forecasts: are the parameters relevant?
AbstractIn this paper, we present a review of various computational experiments â€“ and consequent results â€“ concerning Neural Network (NN) models developed for regional employment forecasting. NNs are widely used in several fields because of their flexible specification structure. Their utilization in studying/predicting economic variables, such as employment or migration, is justified by the ability of NNs of learning from data, in other words, of finding functional relationships â€“ by means of data â€“ among the economic variables under analysis. A series of NN experiments is presented in the paper. Using two data sets on German NUTS 3 districts (326 and 113 labour market districts in the former West and East Germany, respectively), the results emerging from the implementation of various NN models â€“ in order to forecast variations in full-time employment â€“ are provided and discussed In our approach, single forecasts are computed by the models for each district. Different specifications of the NN models are first tested in terms of: (a) explanatory variables; and (b) NN structures. The average statistical results of simulated out-of-sample forecasts on different periods are summarized and commented on. In addition to variable and structure specification, the choice of NN learning parameters and internal functions is also critical to the success of NNs. Comprehensive testing of these parameters is, however, limited in the literature. A sensitivity analysis is therefore carried out and discussed, in order to evaluate different combinations of NN parameters. The paper concludes with methodological and empirical remarks, as well as with suggestions for future research.
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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 Regional Employment Forecasts: Are the Parameters Relevant?," Working Paper Series, The Rimini Centre for Economic Analysis 07_09, The Rimini Centre for Economic Analysis, revised Feb 2010.
- 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, USI UniversitÃ della Svizzera italiana 0903, USI UniversitÃ della Svizzera italiana.
- Patuelli, R. & Reggiani, A. & Nijkamp, P. & Schanne, N., 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
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