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This study investigates the impact of the selected financial development proxies and foreign direct investment (FDI) on the growth in the case of Turkey, using annual data for the 1960–2011 period. The second-generation econometric procedure has been applied for the first time to the Turkish data with this respect. Unit root tests by Carrion-i-Silvestre et al. (2009) assume that real income, financial development proxies, and FDI are non-stationary at levels, but become stationary at first differences through multiple structural breaks. Cointegration results by Maki (2012) confirm the existence of a long-term equilibrium relationship between real income growth, financial development, and FDI, again through multiple structural breaks. Finally, this paper confirms that financial development and FDI are long-term drivers of real income, which enable it to react to its long-term path significantly.

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Discussion on methodological problems of corporate survival and solvency prediction is enjoying a renaissance in the era of financial and economic crisis. Within the framework of this article, the most frequently applied bankruptcy prediction methods are competed on a Hungarian corporate database. Model reliability is evaluated by Receiver Operating Characteristic (ROC) curve analysis. The article attempts to answer the question of whether the simultaneous application of data reduction and univariate splitting (or just one of them) improves model performance, and for which methods it is worth applying such transformations.

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In our work, we compare the predictive power of different bankruptcy prediction models built on financial indicators calculable from businesses’ accounting data on the database of the first Hungarian bankruptcy model. For modelling, we use data-mining methods often applied in bankruptcy prediction: neural networks (NN), support vector machines (SVM) and the rough set theory (RST) capable of rule-based classification. The point of departure for our comparative analysis is the practical finding that black-box-type data-mining methods typically show better classification performance than models whose results are easy to interpret, i.e. there seems to be a kind of trade-off between the interpretability and predictive power of bankruptcy models. Empirical results lead us to conclude that the RST approach can be a competitive alternative to black-box-type SVM and NN models. In our research, we did not find any major trade-off between the interpretability and predictive performance of bankruptcy models on the database of the first Hungarian bankruptcy model.

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Acta Oeconomica
Authors: José Augusto Lopes Da Veiga, Alexandra Ferreira-Lopes, Tiago Neves Sequeira, and Marcelo Serra Santos

In this paper we analyse the role of the traditional determinants of economic growth in the African countries in the period between 1950 and 2012. Due to the specificity and the single nature of each one of these countries, methods that take into account observed and unobserved heterogeneity are used. Results highlight the relevance of the growth rate of the capital stock to growth in the short-run, which is significant in all regressions. The growth rate of the government to GDP ratio is also important in all but one of the regressions in which it appears, and its growth is harmful for the growth of GDP per capita in the short-run. The variables related to public debt do not present any relationship with economic growth. Human capital has a positive relationship with economic growth in regressions that do not include public debt. The growth rate of real GDP per capita also depends (negatively) on its past value, i.e., the lower the real GDP per capita the higher will be its growth rate.

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When calculating different profitability measures for a life insurance company, one of the most important parameters to know is the probability of a policy being in force at any given time after the start of risk bearing. These probabilities are given by the survival function. In this paper, we examine data from a Hungarian insurance company, in order to build models for the survival functions of two life insurance products. For survival function estimation based on the unique parameters of a new policy, Cox regression is used. However, not all parameters of a new policy are relevant in estimating the survival function. Therefore, application of model selection algorithms is needed. Furthermore, if the exact effects of the policy parameters for the survival function can be determined, the insurance company can direct its sales team to acquire policies with positive technical results. When traditional model selection techniques proposed by the literature (such as best subset, stepwise and regularization methods) are applied on our data, we find that the effect of the selected predictors for survival cannot be determined, as there is a harmful degree of multicollinearity. In order to tackle this problem, we propose adding the hybrid metaheuristic from Láng et al. (2017) to the Cox regression in order to eliminate multicollinearity from the final model. On the test sets, performance of the models from the metaheuristic rivals those of the traditional algorithms with the use of noticeably less predictors. These predictors are not significantly correlated and are significant for survival, as well. It is shown in the paper that with the application of metaheuristics, we could produce a model with good predicting capabilities and interpretable predictor effects. These predictor effects can be used to direct the sales activities of the insurance company.

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In our study we rely on a data mining procedure known as support vector machine (SVM) on the database of the first Hungarian bankruptcy model. The models constructed are then contrasted with the results of earlier bankruptcy models with the use of classification accuracy and the area under the ROC curve. In using the SVM technique, in addition to conventional kernel functions, we also examine the possibilities of applying the ANOVA kernel function and take a detailed look at data preparation tasks recommended in using the SVM method (handling of outliers). The results of the models assembled suggest that a significant improvement of classification accuracy can be achieved on the database of the first Hungarian bankruptcy model when using the SVM method as opposed to neural networks.

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The paper analyses the impact of the factors of production on economic growth in Poland in the years 1992–2012, with particular focus on the impact of foreign direct investment (FDI), and strives to verify whether a causality relationship occurred between GDP and FDI, i.e. whether high GDP dynamics attracted FDI inflows and whether this investment contributed to GDP growth. The Vector Error Correction Method impulse responses and variance decomposition analysis confirmed the bi-directional relationships between FDI and GDP in Poland. However, the impact of GDP on attracting FDI inflows to Poland is stronger than that of FDI on GDP growth. Polish developmental policy should concentrate on three essential determinants (pillars) of growth, namely employment growth, attracting FDI (with emphasis on improvement in the type of inflowing investment), and increasing the value and productivity of domestic investment.

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Abstract  

The compound BR-A657 is an angiotensin II receptor antagonist. The objective of this work was to investigate the existence of polymorphs and pseudopolymorphs of BR-A657 and the transformation of crystal forms. Three crystal forms of BR-A657 have been isolated by recrystallization and characterized by powder X-ray diffractometry, differential scanning calorimetry, and thermogravimetric analysis. After storage of three days at 0% RH (silica gel, 20°C), 52% RH (saturated solution of Na2Cr2O7·2H2O/20°C) and 95% RH (saturated solution of Na2HPO4/20°C), Forms 2 and 3 were transformed to Form 1.

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Summary  

This paper describes the use of the mass spectrometry (MS), thermal analyses (TA) and other physico-chemical methods to investigate the structure of two newly synthesized phenolic-iodine derivative polymeric products. These two products are formed as a result of redox-interaction of adrenaline hydrogen tartrate (AHT, I) with iodate (IO- 3) and periodate (IO- 4). The characterization of the two products were achieved satisfactorily by using the above tools and their proposed general formulae, were found to be C52H67O36N4I (AHT- IO- 3, II) and C26H34O18N2I2(AHT- IO- 4, III). The fragmentation behavior of the main compound (AHT) in MS and TA (TG and DTA) techniques was investigated and compared. The results obtained were used to explain the fragmentation of the products AHT- IO- 3and AHT- IO- 4in mass spectrometry and thermal analyses techniques. The stabilities of different fragments were discussed. The results indicate that the two techniques are supporting each other in which the mass spectrometry provides the structural information in gas phase while the thermal analyses provides the quantitative fragmentation in the solid-state.

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Abstract  

The extraction of cesium, strontium and barium by a nitrobenzene solution of dicarbollylcobaltate in the presence of various crown ethers has been investigated and the influence of the substituent of crown to the extraction possibility has been observed. It has been found, that the addition of DB15C5, DB18C6 and DB21C7 (but not DB24C8 and DCH24C8) increases the distribution ratio of Cs by one order of magnitude. The fivemembered crowns are usually more efficient extractants for strontium than DCH18C6, which is widely used for strontium separation. The distribution ratio of strontium DSr decreases in the order 15-crown-5>benzo-15-crown-5>2-hydroxymethyl-15-crown-5>cyclohexyl-15-crown-5 >dibenzo-15-crown-5> nitrobenzo-15-crown-5. The selectivity α(Sr/Ca) decreases in the order 15C5>B15C5>DB15C5>2HM15C5>CH15C5. A selectivity factor α(Sr/Ca)≥1000 can be reached in the presence of 15C5 and B15C5. Six-membered and four-membered crowns extract strontium worse than most of the five-membered crowns. The selectivity factors α(Sr/Ca)≈100 have been reached for six-membered crowns and α(Sr/Ca)<1 has been found for 12C4. The extraction of barium by a nitrobenzene solution of dicarbollylcobaltate in the presence of 15C5 is even more efficient as the extraction of strontium. In that system DSr>104 and DBa>104 have been found for the extraction of Sr and Ba by a 0.01M nitrobenzene solution of dicarbollylcobaltate (cB=0.01 mol/l) from 0.1M HNO3. Maximal values of separation factor α(Ba/Sr) have been found in the system containing DB21C7.

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