E-learning Skill and Use in EU Countries – A Statistical Analysis

The article investigates the division between member states of the European Union considering the aspect of their level of information and communication technology (ICT) development focusing on e-learning. With the help of discriminant analysis the countries are categorized into groups based on their ICT maturity and e-learning literacy level of development. Making a comparison with a benchmarking tool, the ITU (International Telecommunication Union)’s ICT Development Index (IDI) the results are confirmed partly correct. The article tries to find economical explanations for the re-grouping of the countries ranking. Finally the author examines the reliability of Hungary’s ranking results and the factors which may affect this divergence from the real picture.

Inventory investment and sectoral characteristics in some OECD countries

This paper discusses the effects of sectoral structure on the long run macroeconomic inventory behaviour of national economies. Data on 15 OECD countries are included in the analysis, which is based on correlation and cluster analysis methodologies. The study is part of a long-term research project exploring factors influencing the inventory behaviour of national economies. First, we introduce some basic characteristics of macroeconomic inventory formation in the 15 OECD countries. We argue that our previous results on the existence of specific characteristic features of macroeconomic inventory investment are justified, hence it makes sense to study the factors influencing these features. We then examine the contribution of various sectors to the production of in the countries involved and the relationship between sectoral structure and inventory intensity (annual inventory change/Gross Value Added). We find that the high share of agriculture and manufacturing increases inventory intensity, that the increasing share of services has a negative effect and that the role of construction and trade is not obvious. The relatively low stability of the statistical results warns us to be cautious with our judgements. Further, case-by-case analysis would be required to obtain more solid results.

Neural Networks in Bankruptcy Prediction - A Comparative Study on the Basis of the First Hungarian Bankruptcy Model

The article attempts to answer the question whether or not the latest bankruptcy prediction techniques are more reliable than traditional mathematical–statistical ones in Hungary. Simulation experiments carried out on the database of the first Hungarian bankruptcy prediction model clearly prove that bankruptcy models built using artificial neural networks have higher classification accuracy than models created in the 1990s based on discriminant analysis and logistic regression analysis. The article presents the main results, analyses the reasons for the differences and presents constructive proposals concerning the further development of Hungarian bankruptcy prediction.