When the investors decide to make a foreign direct investment, they take various factors into consideration such as political risk. In the study that covers the years 2002-2012 and data from 91 countries, the impact of political risk on foreign direct investment has been demonstrated by conducting panel data analysis. Political risk and control variables have been used. An increase in "political stability and absence of violence" and "management effectiveness" has reduced the foreign direct investment. Moreover, a rise in the variables of the "exportation of goods and services", "population", "GDP growth", "regulatory quality" has increased the foreign direct investment.

While various linear and nonlinear forecasting models exist, multivariate methods like VAR, Exponential smoothing, and Box-Jenkins' ARIMA methodology constitute the widely used methods in time series. This paper employs series of Turkish private consumption, exports and GDP data ranging between 1998: Q1 and 2017: Q4 to analyze the forecast performance of the three models using measures of accuracy such as RMSE, MAE, MAPE, Theil's U 1 and U 2 . Seasonal decomposition and ADF unit root tests were performed to obtain new deseasonalized series and stationarity, respectively. Results offer preference for the use of ARIMA in forecasting, having performed better than VAR and exponential smoothing in all scenarios. Additionally, VAR model provided better forecast accuracy than exponential smoothing on all measures of accuracy except on Thiel's U 2 whose VAR values were not computed. Cautionary use of ARIMA for forecasting is recommended.

Bu çalışmada OECD üyesi ülkelerin 2001-2018 yılları arasında birbirleri ile yaptıkları ticaret ağ analizi yöntemi ile incelenmiştir. Bu bağlamda, OECD'nin ticari ağ yapısının yıllara göre gösterdiği değişimin ortaya konulması, ülkelerin birbirleriyle kurdukları ticari ilişkilerin yoğunluğu ya da zayıflığının belirlenmesi, hangi ülkelerin istikrarlı olup ağdaki yerini koruduğunun ve ağ içerisinde merkez ve otorite ülkenin hangi üye ülke olduğunun ortaya konulması amaçlanmıştır. Çalışmanın verileri Trademap veri tabanından elde edilmiş olup bu verilerin analizleri UCINET programı ile gerçekleştirilmiştir.

This paper investigates the formation of convergence clubs and examines the drivers of growth convergence in Africa by accounting for individual heterogenous effects and establishing transitional paths. We particularly employ the sophisticated log t test to identify underlying convergence clubs and use LSDVC as a benchmark model for analysing the drivers of convergence. We also apply the System Generalized method of moments (GMM) model for sensitivity purposes. Our results reveal four core convergence clubs; seemingly characterised by the measures of institutional stability with distinct transitional paths. We consequently highlight the importance of initial conditions, human capital and institutions in the formation of convergence clubs. Thus, the paper provides insights into the adoption of differentiated development policies consistent with the specific conditions of African countries with the integration agenda driven by accelerated levels of human capital development and technological progress.

Our paper explores the prospects for the proposed East African Monetary Union (EAMU) by employing rigorous empirical tools to analyse business cycles synchronisation, structural cross‐correlations, spectral decomposition and regional clusters to identify different cyclical episodes, periodicities and characterise the economic cycles of East African countries. We find that cyclical movements reflect various idiosyncratic, common, historical and external shocks in the region. Secondly, all countries appear to be structurally correlated with each other except for South Sudan and Burundi. Our results also observe that the contemporaneous co‐movements of East African Community (EAC) cycles with those of Kenya and Tanzaniaare procyclical with coincidental path shift, while the same EAC cycles appear to be acyclical with those of Burundi. Additionally, from the spectral decomposition, Kenyan cycles take 10 years to complete, while those of Tanzania and Rwanda take 8 years. Ugandan and Burundian cycles take approximately 5 years, while the cyclical frequency for South Sudan corresponds to 3.3 years. Finally, the cluster characterisation of countries reveals that South Sudan, Burundi and Rwanda form a group, while Kenya and Tanzania from a group distinct from the rest. We urge the member countries to prioritise policies on regional risk‐sharing and adjustment mechanisms, in addition to establishing credible institutional infrastructure that ensures surveillance and enforcement of convergence conditions adopted in EAMU protocol.