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...

This paper develops a flexible multi-dimensional assessment method for the comparison of different statistical-econometric techniques based on learning mechanisms, with a view to analysing and forecasting regional labour markets. The aim of this paper is twofold. A first major objective is to...

This paper offers an overview of experimental results, based on neural networks (NNs) used to forecast regional employment variations in Germany. NNs are statistical optimization tools inspired by the functioning of biological neural networks. Their main characteristics are their non-linear and...

The analysis of the structure and evolution of complex networks has recently received considerable attention. Although research on networks originated in mathematical studies dating back to the nineteenth century (or earlier), and developed further in the mid-twentieth century with contributions...

In this paper, a set of neural network (NN) models is developed to compute short-term forecasts of regional employment patterns in Germany. NNs are modern statistical tools based on learning algorithms that are able to process large amounts of data. NNs are enjoying increasing interest in...

The present paper investigates, for the case of Germany, the relevance of the volume and distribution of commuting flows, as well as of the commuting network’s connectivity and topology. We aim to assess how network topology and its changes over time affect the geographic commuting system and...