Explaining the Fdi Patterns in Central and Eastern Europe: a Neural Network Approach

Central and Eastern European (CEE) countries are in an economic transition process which involves convergence of economic performance with the European Union. One of the principle engines for the necessary transformation towards EU average economic performance is inward-FDI. Quantitatively examining the causes of FDI in the CEE region is thus an important research area. Traditional linear regression approaches have had difficulty in achieving conceptually and statistically reliable results. In this paper, we offer a novel approach to examining FDI in the CEE region. The key tasks addressed in this research are (i) a neural network based FDI forecasting model and (ii) nonlinear evaluation of the determinants of FDI. The methodology is nontraditional for this kind of research (compared with multiple linear regression estimates) and is applied primarily for the FDI dynamics in the CEE region with some worldwide comparisons. In terms of MSE and R-squared criteria, we find that NN approaches are better to explain FDI determinants’ weights than traditional regression methodologies. Our findings are preliminary but offer important and novel implications for future research in this area, including more detailed comparisons across sectors as well as countries over time.