NDLI: Neural networks for regional employment forecasts: are the parameters relevant?

Content Provider	Springer Nature Link
Author	Patuelli, Roberto Reggiani, Aura Nijkamp, Peter Schanne, rbert
Copyright Year	2010
Abstract	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.
Starting Page	67
Ending Page	85
Page Count	19
File Format	PDF
ISSN	14355930
Journal	Journal of Geographical Systems
Volume Number	13
Issue Number	1
e-ISSN	14355949
Language	English
Publisher	Springer-Verlag
Publisher Date	2010-09-14
Publisher Place	Berlin, Heidelberg
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Neural networks Sensitivity analysis Employment forecasts Local labour markets Simulation and Modeling Quantitative Geography Landscape/Regional and Urban Planning Computer Application in Social and Behavioral Sciences Geographical Information Systems/Cartography Regional/Spatial Science
Content Type	Text
Resource Type	Article
Subject	Earth-Surface Processes Geography, Planning and Development Economics and Econometrics

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