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-016-9337-3 GAGNON, J. E., BAYOUMI, T., LONDONO, J. M., SABOROWSKI, C., SAPRIZA, H., (2017), Direct and spillover effects of unconventional monetary and exchange rate policies, Open Economies Review , Vol. 28, No. 2, pp.191-232. DOI: 10.1007/s11079-017-9437-0 GERTLER, M., KARADI, P., (2011), A model of unconventional monetary policy, Journal of monetary Economics , Vol. 58, No. 1, pp.17-34. DOI: 10.1016/j.jmoneco.2010.10.004 GOLDSTEIN, I., WITMER, J., YANG, J., (2018), Following the Money: Evidence for the Portfolio Balance Channel of Quantitative Easing, Bank of Canada, Staff


This paper analyses the volatility of retail fuel prices in nine different EU countries and the spillover effects between fuel prices across selected countries from Central and Eastern Europe and the Eurozone over the 2008-2019 period. In particular, we use the GARCH-GJR model in order to investigate fuel price volatility and identify potential asymmetric dynamics. Moreover, in order to assess the links between fuel prices across countries, we estimate a VAR model and compute spillover measures using the Generalised Forecast Error Variance Decomposition (GFEVD) approach formulated by Diebold and Yilmaz (2009). Our results provide evidence of weak links between retail fuel prices across EU countries, with slightly higher spillovers originating from some developed economies such as France and Italy.

References Chae, Inyoung; Stephen, Andrew T.; Bart,Yakov and Yao, Dai (2017): “Spillover effects in seeded word-of-mouth marketing campaigns,” Marketing Science , Vol. 36, No. 1, pp. 89 - 104.

References BAELE, L. (2005). Volatility Spillover Effects in European Equity Markets , Journal of Financial and Quantitative Analysis 40(2), Pp. 373-401. BEINE, J., CAPORALE, G.M., GHATTAS, M. S., SPAGNOLO, N. (2010). Global and regional spillovers in emerging stock markets: A multivariate GARCH-in-mean analysis. Emerging Markets Review , 11(2) Pp. 250-260. BEKAERT, G., HARVEY, C.R. (1997). Emerging Equity Market Volatility, Journal of Financial Economics, 43(1), Pp. 29-77. BOLLERSLEV, T. (1986). Generalized Autoregressive Conditional Heteroskedasticity

Johansson, A. C., & Ljungwall, C. (2009). Spillover Effects among the Greater China Stock Markets. World Development, 37(4), 839-851. Kim, S. J., Salem, L., & Wu, E. (2015). The Role of Macroeconomic News in Sovereign CDS Markets: Domestic and Spillover News Effects from the U.S, the Eurozone and China. Journal of Financial Stability, 18, 208-224. King, M., Sentana, E., & Wadhwani, S. (1994). Volatility and Links between National Stock Markets. Econometrica, 62(4), 901-933. https


The main focus of this study is on understanding the importance of social dynamics of cities for attracting human capital to urban regions. The principal research question of the article is “if there is a spatial dependency on neighbouring provinces’ social environmental qualities in human capital attraction for Turkey.” It is believed that developmental disparities among regions can be overcome with a balanced distribution of human capital. In this article, first the concept and importance of human capital and its evolution throughout economic history are explained in order to emphasize the relationship between development and human capital for urban regions. The literature review consists of migration models developed and used in previous studies and recent literature that together consider human capital and its flow with spatial analysis. A review of migration models helps structure the quantitative models’ building blocks, or the concepts to be quantified. Literature that discusses human capital and spatial analysis, at the same time, guides the study in implementing the most appropriate analysis technique. The literature discussed in the paper is focused on human capital migration and urban attractiveness. Its similarity with the current study work is the focus on the relationship between urban environment components and human capital. However, the cited studies lack the “spatial/relational” approach to urban regions which means that the effects of developments in settlements neighbouring the region were ignored. The contribution which we intend to make with the current study is to adapt the spatial econometric analysis to the problem of human capital attraction. Literature review is followed by data used in the empirical part of the study, and brief information on spatial econometric analysis. Next, findings of the empirical spatial econometric analysis of Turkey’s 81 urban regions are provided. Overall, the analysis indicated that undergraduate and post-graduate migrants care about the social prosperity of the neighbouring environment of destination province. The last part concludes with an interpretation of empirical study findings and discusses relevant urban and regional policy instruments.

effectiveness is not. Third, the matrix W is replaced by W * . Note that the row elements of W * , in contrast to those in W , do not necessarily sum up to 1. However, the N − 1 eigenvalues of W * are identical to those of W that remain after the unit eigenvalue of W is excluded. Direct, indirect and spatial spillover effects Following Elhorst et al. (2013) and Yu, de Jong, & Lee, (2012) , the DSPD model in Eq. (1) has the following error correction model (ECM) representation (see also Elhorst, 2014a , b ): (6) Δ Y t = ( I − ρ W ) − 1 [ ( τ − 1 ) I + ( ρ

Innovation Propensity in Croatian Enterprises: Results of a Community Innovation Survey

This paper assesses the determinants of innovation activities in Croatian enterprises and their implications for innovation policy. A Type-2 Tobit model is used for modelling the innovation behaviour of Croatian companies, based on the results of a Community Innovation Survey conducted for the period 2001-2003. This model identified the positive effects of conditions for enterprise growth (enterprise size and demand pull variable) and the integration of enterprises into international flows of capital and goods (through foreign direct investments) as well as R&D activities. These variables can be tackled through a more effective policy framework that should increase competitiveness within industries in order to stimulate the demand for innovation. The focus should be on fostering enterprise growth, attraction of FDI with strong spillover effects, and stimulating the export capability of enterprises. Only in such a context can the positive effects of engagement in R&D be maximised.


The world economy has been developing at a very fast pace for the past few decades, growth which is commonly linked to the development of technology. Innovative ideas become successful when certain individuals decide to face the multiple risks that appear when transforming these ideas in to reality. The vast literature on entrepreneurship has shown that startups are important players in driving the economy on an ascending path. It is no surprise that highly developed countries, such as USA, Israel or Singapore have governmental programs which stimulate startup creation. More recently, the Romanian government has also joined in on spending money to offer entrepreneurs the chance to create successful businesses. Using spatial panel data on the 41 counties of Romania and the capital, Bucharest, on the period 2011-2016, this study highlights some significant dependencies between the survival of startups (for a period of 3 years) and other factors – both internal and external. The analysis shows that the aforementioned survival is clearly and positively impacted by Foreign Direct Investment, the share of fresh businesses in the total business environment and the number of immigrants with a permanent residence in the respective counties. Moreover, there are significant spatial effects occurring between neighboring counties. These results suggest that foreign investors could benefit from bringing their capital in Romania, as startups greatly contribute to the specialization of markets, and moreover, spillover effects present suggest that a smaller number of investment centers can be highly effective in their regions.


Many studies point out the growing correlations within financial markets, while others highlight the financialization of commodity markets. The purpose of this article is to revisit the relationships between various financial assets and commodity markets by taking into account the U.S. monetary policy and therefore the implementation of non-standard measures. In addition to oil, stock and bond markets, U.S. policy rates and a great deal of agricultural prices have been over time considered through a DCC-GARCH model, between 1995-2015. We find that agricultural markets uphold the financialization hypothesis, implying an increase in market-prices’ correlations and so raises the question of agricultural prices’ drivers. Interestingly, conditional correlations between the U.S. monetary policy and agricultural prices have decreased since 2010, which indicates that the implementation of non-standard monetary policy measures reduces spillover effects on asset prices, especially raw commodities. Such a result in turn highlights changing relationships between monetary, financial and physical markets, in a context of very weak policy rates over a long period.