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The aim of this paper is to forecast regional employment patterns in West German regions. After a brief exposition of key labour market issues, Artificial Neural Network (ANN) techniques are proposed as a new tool to generate reliable short term employment forecasts at a regional level. A variety of ANN models are developed and compared. Comparison with methods commonly applied to panel data, such as GMM (Generalised Method of Moments), confirms the ability of ANNs to capture complex data structures in a multi-regional context.

In this paper, a set of neural network (NN) models is developed to compute short-term forecasts of regional employment patterns in Germany. Neural networks are modern statistical tools based on learning algorithms that are able to process large amounts of data. Neural networks are enjoying increasing interest in several fields because of their effectiveness in handling complex data sets when the functional relationship between dependent and independent variables is not specified explicitly. The present paper compares two NN methodologies. First, it uses NNs to forecast regional employment in both the former West and East Germany. Each model implemented computes single estimates of employment growth rates for each German district, with a 2-year forecasting range. Next, additional forecasts are computed, by combining the NN methodology with shift-share analysis (SSA). Since SSA aims to identify variations observed among the labour districts, its results are used as further explanatory variables in the NN models. The data set used in our experiments consists of a panel of 439 German (NUTS 3) districts. Because of differences in the size and time horizons of the data, the forecasts for West and East Germany are computed separately. The out-of-sample forecasting ability of the models is evaluated by means of several appropriate statistical indicators.

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.