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

Patuelli, Roberto ; Reggiani, Aura ; Nijkamp, Peter ; Schanne, Norbert

In: Journal of Geographical Systems, 2011, vol. 13, no. 1, p. 67-85

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    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