Neural Networks for Regional Employment Forecasts: Are the Parameters Relevant?
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.
|Date of creation:||Jan 2009|
|Date of revision:||Feb 2010|
|Publication status:||Published in the Journal of Geographical Systems, 13(1):67–85, 2011|
|Contact details of provider:|| Postal: Via Patara, 3, 47921 Rimini (RN)|
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Working Paper Series
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