Neural networks for regional employment forecasts

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." (Author's abstract, IAB-Doku) ((en))

Cite article

Patuelli, R., Nijkamp, P., Reggiani, A. & Schanne, N. (2011): Neural networks for regional employment forecasts. Are the parameters relevant? In: Journal of Geographical Systems, Vol. 13, No. 1, p. 67-85. DOI:10.1007/s10109-010-0133-5