A rank-order test on the statistical performance of neural network models for regional labor market forecasts

Abstract

"Using a panel of 439 German regions we evaluate and compare the performance of various Neural Network (NN) models as forecasting tools for regional employment growth. Because of relevant differences in data availability between the former East and West Germany, the NN models are computed separately for the two parts of the country. The comparisons of the models and their ex-post forecasts are carried out by means of a non-parametric test: viz. the Friedman statistic. The Friedman statistic tests the consistency of model results obtained in terms of their rank order. Since there is no normal distribution assumption, this methodology is an interesting substitute for a standard analysis of variance." (Author's abstract, IAB-Doku) ((en))

Cite article

Patuelli, R., Longhi, S., Reggiani, A., Nijkamp, P. & Blien, U. (2007): A rank-order test on the statistical performance of neural network models for regional labor market forecasts. In: The Review of Regional Studies, Vol. 37, No. 1, p. 64-81.

Further information

earlier released (possibly different) as: A rank-order analysis of learning models for regional labor market forecasting. Paper presented at the North American Regional Science Conference 2004, 2005, 18 S.