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

Authors :
Patuelli, Roberto
Reggiani, Aura
Nijkamp, Peter
Schanne, Norbert
Source :
Journal of Geographical Systems; Mar2011, Vol. 13 Issue 1, p67-85, 19p, 4 Charts, 1 Graph
Publication Year :
2011

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. [ABSTRACT FROM AUTHOR]

Details

Language :
English
ISSN :
14355930
Volume :
13
Issue :
1
Database :
Complementary Index
Journal :
Journal of Geographical Systems
Publication Type :
Academic Journal
Accession number :
58504607
Full Text :
https://doi.org/10.1007/s10109-010-0133-5