Abstract
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 (H) nonlinear evaluation of the determinants of FDI. The methodology is non traditional 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 Rsquared 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.
Highlights
Since the process of economic, political and social transformation began in 1989, scholars from a range of academic disciplines have emphasized the primordial role to be played by investment - in particular Foreign Direct Investment (FDI) - in the economic deveJopment of the Central and East European region
As we demonstrate in sections below, the main innovation in this paper is the use of non-linear dynamics, since we believe that the phenomenon ofFDI itself is neither statistically linear nor conceptually static
We have noticed the influence of inconsistencies and incompleteness in the initial data set on the neural network (NN) performance, which is more sensitive in this respect than multiple linear regression (MLR)
Summary
Since the process of economic, political and social transformation began in 1989, scholars from a range of academic disciplines have emphasized the primordial role to be played by investment - in particular Foreign Direct Investment (FDI) - in the economic deveJopment of the Central and East European region. This paper aims to offer an alternative and potentially pathbreaking new methodology using neural network (NN) modeling approaches to examining the determinants of FDI It is explicitly non-linear in its approach and allows for the modeling of non-linear empirical phenomena without the need to impose linearity on data for the purposes of regression analyses. Before we turn to the methodology in more detail in sections, the following section offers a brief literature review of FDI empirical research in the CEE region and a summary of key conceptual issues that flow from this. This will enable us to examine the methodological approach we adopt in our paper
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