Explaining foreign direct investment patterns in Central and East Europe: A neural network approach

Darius Plikynas*, Yusaf H. Akbar, Zelma Echeverria

*Corresponding author for this work

Research output: Contribution to Book/Report typesConference contributionpeer-review

Abstract (may include machine translation)

Quantitatively examining the 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. 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. In terms of MSE and R-squared criteria, we find 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.

Original languageEnglish
Title of host publicationProceedings of the 2005 International Conference on Artificial Intelligence, ICAI'05
Pages53-59
Number of pages7
StatePublished - 2005
Externally publishedYes
Event2005 International Conference on Artificial Intelligence, ICAI'05 - Las Vegas, NV, United States
Duration: 27 Jun 200530 Jun 2005

Publication series

NameProceedings of the 2005 International Conference on Artificial Intelligence, ICAI'05
Volume1

Conference

Conference2005 International Conference on Artificial Intelligence, ICAI'05
Country/TerritoryUnited States
CityLas Vegas, NV
Period27/06/0530/06/05

Keywords

  • Foreign direct investment
  • Neural network applications
  • Nonlinear systems

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