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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. Furthermore, the Friedman statistic is indifferent to the scale on which the data are measured. The evaluation of the ex post forecasts suggests that NN models are generally able to correctly identify the fastest-growing and the slowest-growing regions, and hence predict rather well the correct ranking of regions in terms of their employment growth. The comparison among NN models – on the basis of several criteria – suggests that the choice of the variables used in the model may influence the model’s performance and the reliability of its forecasts.

This study analyses the effect of population ageing in the ten federal states (Lander) of West Germany during the period ranging from 1970 to 2014. To that aim, we estimate a Bayesian VAR model including wages, labour force participation rates, unemployment rates and the share of population above 65 years. Structural regional labour market shocks are identified employing a combination of zero and sign restrictions. Our findings suggest that shocks to population ageing on impact, increase the participation rate, the unemployment rate and decrease wages. However, the above effects on participation and unemployment are short-lasting and after a few periods their response is inverted. Our variance decomposition analysis reveals that the most relevant channel of impact on the economy implied by ageing is that of wages.