This article focuses on the problem of binary classification of 902 small- and medium‑sized engineering companies active in the EU, together with additional 51 companies which went bankrupt in 2014. For classification purposes, the basic statistical method of logistic regression has been selected, together with a representative of machine learning (support vector machines and classification trees method) to construct models for bankruptcy prediction. Different settings have been tested for each method. Furthermore, the models were estimated based on complete data and also using identified artificial factors. To evaluate the quality of prediction we observe not only the total accuracy with the type I and II errors but also the area under ROC curve criterion. The results clearly show that increasing distance to bankruptcy decreases the predictive ability of all models. The classification tree method leads us to rather simple models. The best classification results were achieved through logistic regression based on artificial factors. Moreover, this procedure provides good and stable results regardless of other settings. Artificial factors also seem to be a suitable variable for support vector machines models, but classification trees achieved better results using original data.

This article deals with the development of technical (production) efficiency in the metallurgical industry in EU countries with an emphasis on the situation in the Czech Republic. The efficiency of individual countries was estimated for the period from 1995 to 2015. The parametric stochastic frontier analysis method with different settings was chosen to estimate efficiency and the results were verified using a competitive non-parametric data envelopment analysis method. It was found that during the period under review, there was an average increase in efficiency in the metallurgical industry. The largest increase in efficiency (confirmed by all types of models) was observed in the Czech Republic. A visible positive efficiency shift was also recorded in Spain and Greece. Surprisingly, there has been a decline in efficiency in Sweden and Italy.

The forestry sector is facing critical challenges due to climate change. Decision-making support based on efficiency evaluation using non-parametric methods could provide important information for both forest managers and policymakers. However, such advanced technical analysis is scarce in forestry science. When applied, its application has been primarily based on aggregated, macro-level data, and efficiency was analysed for the forestry sector as a whole. There is a lack of studies from the company-level perspective, which are needed to provide sound decision support.In this paper, we focus on the micro-data level and offer the data envelopment analysis model settings and interpretations for an efficiency evaluation based on the financial data of individual forestry companies. The aim is to provide an original analysis of the company-level driving forces of forestry sector efficiency. The results for central European countries show that efficiency is driven by company size and country of operation. The study also confirms that, generally, German companies are the »efficiency leaders« in the region, while Czech companies may serve as an efficiency reference for east-central European forestry companies.

This article deals with the comparison of technical efficiency results through various stochastic frontier analysis models. Effects of model type, possible frontier shift, distribution of inefficiency term, different output variable, and estimator selection were explored. For this purpose, aggregated annual data within the EU construction sector from 2000 to 2015 were used for the efficiency estimation. The resulting efficiency values were compared by correlation coefficients. Among others, it has been shown that estimator selection may strongly affect efficiency estimate for particular models.

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