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...

For several decades, theorists and researchers have emphasized investigating and analyzing the likelihood of contagion of turbulence among markets. The behavior of the housing market is also essential because of the impact of housing prices on the loan portfolios of banks and other financial...

Empirical assessments of the forecasting power of spatial panel data econometric models are still scarcely available. Moreover, several methodological contributions rely on simulated data to showcase the potential of proposed methods. While simulations may be useful to evaluate the properties of...

Population changes are decisive for growth performances. This has been shown in a number of country studies, using time series data. The analysis is here extended in two dimensions: 1) the importance of demographics for growth is taking in to account a regional dimension allowing for spatial...

The paper attempts to determine regional unemployment rates for NUTS 2 regions based on data available on a regular basis from Eurostat and ILO. In the first part it is shown how the evolution of regional unemployment rates is determined by national unemployment rates by industry and the...

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...

It is broadly accepted that two aspects regarding the modeling strategy are essential for the accuracy of forecast: a parsimonious model focusing on the important structures, and the quality of prospective information. Here, we establish a Global VAR framework, a technique that considers a...

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...

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...