C31 - Multiple or Simultaneous Equation Models: Cross-Sectional Models; Spatial Models; Treatment Effect Models; Quantile Regressions; Social Interaction ModelsReturn

The paper presents various GLM models using individual rating factors to calculate the solvency capital requirements for non-life underwriting risk in insurance. First, we consider the potential heterogeneity of claim frequency and the occurrence of large claims in the models. Second, we analyse how the distribution of frequency and severity varies depending on the modelling approach and examine how they are projected into SCR estimates according to the Solvency II Directive. In addition, we show that neglecting of large claims is as consequential as neglecting the heterogeneity of claim frequency. The claim frequency and severity are managed using generalized linear models, that is, negative-binomial and gamma regression. However, the different individual probabilities of large claims are represented by the binomial model and the large claim severity is managed using generalized Pareto distribution. The results are obtained and compared using the simulation of frequency-severity of an actual insurance portfolio.

To express human capital of nations proxies, such as literacy rates, school-enrollment rates or years of schooling are used. The aim of this article is to explore another possibility: to relate country's human capital to its outcome in TIMSS (The Third International Mathematics and Science Study), large international study of students' achievements in mathematics and science literacy from 1995. The relationship between TIMSS scores and GDP growth during 2000-2010 and GDP per capita in 2010 is examined and TIMSS are compared with other proxies of human capital, namely primary, secondary and tertiary school-enrollment rates from 1990, 1995 and 2000. The main result is that the correlation between TIMSS scores and GDP per capita in 2010 is statistically significant at ? = 0.01 level, and this relationship is stronger than that for school-enrollment rates. Also, linear models explaining GDP growth with TIMSS were found more statistically significant than models without TIMSS. These results indicate that TIMSS scores might be considered a suitable proxy for nations' human capital after one or one and a half decade.

This paper empirically examines the determinants of economic growth and convergence in transitive economies of Central and Eastern Europe in the 1990s. While the cross-section regression suggests the absence of a significant convergence across the EU15 and other transitive economies, the Visegrad four (Slovakia, the Czech Republic, Hungary and Poland) dummy being positive and significant indicates that this group of countries has done relatively better than the other group of transitive economies. Moreover, the results indicate that there was an income per capita convergence within Visegrad countries. Switching to a panel data approach, and controlling for macroeconomic stability, financial development, human and physical capital accumulations and other policy variables, the results seem to suggest that there was a conditional convergence across EU15 and transitive economies in the 1990s.