Economic growth is one of the ultimate goals of any economic system. This article examines the question whether in 16 transition economies from Central and South Eastern Europe the banking sector influences economic growth. The empirical investigation was carried out using a generalised method of moments (GMM) dynamic panel method. We measure the development in the banking sector using the bank credit to the private sector, interest rates, and ratio of quasi money (RQM). The research results show that credit to the private sector and interest margin (IM) are negatively related to the economic growth, while RQM is positively related to economic growth.

The main purpose of this study is to identify determinants of the demand of life insurance in 14 countries in Central and South- Eastern Europe (CSEE). We use fixed-effects panel model for the period 1998 - 2010 allowing each cross-sectional unit to have a different intercept term serving as an unobserved random variable that is potentially correlated with the observed regressors. We use two measures as a demand for life insurance: life insurance penetration and life insurance density. The research results show that higher, GDP per capita, inflation, health expenditure, level of education and rule of law are the most robust predictors of the use of life insurance. Real interest rates, ratio of quasi-money, young dependency ratio, old dependency ratio control of corruption and government effectiveness do not appear to be robustly associated with life insurance demand.

This study analyses the linkages between macroeconomic and bank-specific determinants of non-performing loans (NPLs) and their impact on macroeconomic performance in the Baltic States using two complementary approaches. First, we examine the macroeconomic and bank-specific determinants of NPLs for a panel of 27 banks from the Baltics using annual data for the period 2005-2014. The most important macroeconomic factors are GDP growth, inflation and domestic credit to the private sector. As for the bank-specific determinants, we found that the equity to total assets ratio, return on assets, the return on equity and the growth of gross loans were of importance. Second, we investigate the feedback between NPLs and its macroeconomic determinants. The results suggest that the real economy responds to NPLs and that there are strong feedback effects from macroeconomic conditions such as domestic credit to private sector, GDP growth, unemployment and inflation to NPLs. ARTICLE HISTORY

The purpose of this paper is to explore the influence of bankspecific and macroeconomic determinants of all non-performing loans (NPLs) to enterprises and households in the Republic of Macedonia. The analysis is performed for the whole banking sector for the period 2003Q4 to 2014Q4, by applying the Autoregressive Distributed Lag Modelling Approach (ARDL), the co-integration model implementing quarterly time series. The results of the research indicate that the profitability of banks, the growth of loans to enterprises and to households respectively, as well as the growth of GDP, all have a negative impact, while banks' solvency and unemployment have a positive impact on the rise of non-performing loans in both models. In addition, regarding enterprises, we found that the exchange rate has a positive and statistically significant impact on the level of NPLs, while inflation has a negative and statistically significant impact on the increase in non-performing loans to households. The main contribution of this paper is that the results obtained by econometric analysis may be used for forecasting non-performing loans several years in the future, as well as for stress-testing both the entire banking system and the individual banks operating in the Republic of Macedonia.

The purpose of this paper is to examine the impact of insurance and economic growth, with empirical analysis for the Republic of Macedonia. We apply multiple regression and control for other relevant determinants of economic growth. The analysis used data for the period 1995 - 2010. In order to solve the model in the analysis will use the technique of least squares, followed by analysis of variability in order to identify the effects of each variable. Insurance development is measured by insurance penetration (insurance premiums in percentage of GDP). We used three different insurance variables - life insurance, non-life insurance and total insurance penetration. According to our findings, insurance sector development positively and significantly affects economic growth. The results are confirmed in terms of non-life insurance, and, total insurance, while the results show that life insurance negatively affect economic growth.