Application of modified neural network weights' matrices explaining determinants of foreign investment patterns in the emerging markets

Abstract

Quantitatively examining determinants of foreign direct investment (FDI) in Central and East Europe (CEE) is an important research area. Traditional linear regression approaches have had difficulty in achieving conceptually and statistically reliable results. The key tasks addressed in this research are a neural network (NN) based (i) FDI forecasting model and (ii) nonlinear evaluation of the determinants of FDI, We have explored various modified backprop NN weights' matrices and distinguished some nontraditional NN topologies. In terms of MSB and R-squared criteria, we found and checked relationship between modified NN input weights and FDI determinants weights. Results indicate that NN approaches better able 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. © Springer-Verlag Berlin Heidelberg 2005.