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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 in the paper, using two data sets on German NUTS-3 districts. Individual forecasts are computed by our models for each district in order to answer the following question: How relevant are NN parameters in comparison to NN structure? Comprehensive testing of these parameters is limited in the literature. Building on different specifications of NN models-in terms of explanatory variables and NN structures-we propose a systematic choice of NN learning parameters and internal functions by means of a sensitivity analysis. Our results show that different combinations of NN parameters provide significantly varying statistical performance and forecasting power. Finally, we note that the sets of parameters chosen for a given model specification cannot be light-heartedly applied to different or more complex models. [ABSTRACT FROM AUTHOR]

This paper develops and applies neural network (NN) models to forecast regional employment patterns in Germany. Computer-aided optimization tools that imitate natural biological evolution to find the solution that best fits the given case (namely, genetic algorithms, GAs) are also used to detect the best NN structure. GA techniques are compared with more `traditional' techniques which require the supervision of experienced analysts. We test the performance of these techniques on a panel of 439 districts in West and East Germany. Since the West and East datasets have different time spans, the models are estimated separately for West and East Germany. The results show that the West and East NN models perform with different degrees of precision, mainly because of the different time spans of the two datasets. Automatic techniques for the choice of the NN architecture do not seem to outperform selection procedures based on the supervision of expert analysts. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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This article analyzes neural neural networks (ANNs) as a method to compute employment forecasts at a regional level The empirical application is based on employment data collected for 327 West German regions over a period of fourteen years. First, the authors compare ANNs to models commonly used in panel data analysis. Second, they verify, in the case of panel data, whether the common practice of combining forecasts of the computed models is able to produce more reliable forecasts. The technique currently employed by the German authorities to compute such regional employment forecasts is comparable to a simple naïve no-change model. For this reason, ANNs are also compared to this undemanding technique. [ABSTRACT FROM AUTHOR]

ABSTRACT The paper focuses on the Transalpine Freight Transport systems in the light of the future integration of single national transport systems into the European transport network. The environmental, social and institutional peculiarities of this 'region' have favoured--in the past--the development of strong nationally-oriented policies, which are largely in contrast with the goals promoted by the European Union. The present analysis aims to highlight opportunities and limits inherent in the implementation of various new network projects, with a particular view on the planned changes of the Alpine transport system. In this framework, a concise description of the existing and 'planned' situation will be offered. In addition, some new forecasting analyses for road transport will be provided on the basis of environmentally-based transport scenarios. In particular, given the large size of our database on European transport flows, two different approaches will be compared, viz. the logit model and the neural network model. Logit models are well-known in the literature; however, applications of logit analysis to large samples are more rare. Neural networks are nowadays receiving a considerable attention as a new approach that is able to capture major patterns of spatial flows, on the basis of fuzzy and incomplete information. The tentative results of both approaches in this context may then be used as a benchmark for judging the results of other transport flow models and offer also a more 'flexible' range of results to policy actors. Furthermore, our study will present the assessment of trans-European freight flows based on interesting future scenarios related to further congestion and the introduction of eco-taxes on transport in Europe. [ABSTRACT FROM AUTHOR]

Neural networks are becoming popular analysis tools in spatial research, as is witnessed by various applications in recent years. The performance of neural network analysis needs to be carefully judged, however, since the theoretical underpinning of neuro-computing is still weakly enveloped. In the present paper we will use the logit model as a benchmark for evaluating the result of neural network models, based on an empirical case study from Italy. The present paper aims to assess the foreseeable impact of the high-speed train in Italy, by investigating competition effects between rail and road transport modes. Two statistical models will then be compared, viz. the traditional logit model and a new technique for information processing, viz. the feedforward neural network model. In the study two different cases -- corresponding to a different set of attributes -- are investigated, namely by using only 'time' attributes and by using both 'time' and 'cost' attributes. From an economic viewpoint, both models appear to highlight the advantage of introducing the high-speed train system in that they show high probabilities of choosing the improved rail transport mode. The feedforward neural net model seems to provide reasonable predictions compared to those obtained by means of a logit model. An important lesson however, is that it is important to define properly the neural network architecture and to train sufficiently the network during the learningphase. [ABSTRACT FROM AUTHOR]