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 – 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.
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Bibliographic InfoPaper provided by The Rimini Centre for Economic Analysis in its series Working Paper Series with number 07_09.
Date of creation: Jan 2009
Date of revision: Feb 2010
Publication status: Published in the Journal of Geographical Systems 13 (1): 67–85
neural networks; sensitivity analysis; employment forecasts; local labour markets;
Other versions of this item:
- Roberto Patuelli & Aura Reggiani & Peter Nijkamp & Norbert Schanne, 2011. "Neural networks for regional employment forecasts: are the parameters relevant?," Journal of Geographical Systems, Springer, vol. 13(1), pages 67-85, March.
- 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.
- 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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- Roberto Patuelli & Daniel A. Griffith & Michael Tiefelsdorf & Peter Nijkamp, 2009.
"Spatial Filtering and Eigenvector Stability: Space-Time Models for German Unemployment Data,"
Working Paper Series
02_09, The Rimini Centre for Economic Analysis, revised May 2010.
- Roberto Patuelli & Daniel A. Griffith & Michael Tiefelsdorf & Peter Nijkamp, 2011. "Spatial Filtering and Eigenvector Stability: Space-Time Models for German Unemployment Data," International Regional Science Review, , vol. 34(2), pages 253-280, April.
- 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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Tinbergen Institute Discussion Papers
06-020/3, Tinbergen Institute.
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- Roberto Patuelli & Simonetta Longhi & Aura Reggiani & Peter Nijkamp, 2008.
"Neural networks and genetic algorithms as forecasting tools: a case study on German regions,"
Environment and Planning B: Planning and Design,
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- Roberto Patuelli & Simonetta Longhi & Aura Reggiani & Peter Nijkamp, 2005. "Forecasting Regional Employment in Germany by Means of Neural Networks and Genetic Algorithms," Computational Economics 0511002, EconWPA.
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