Machine learning and regularization technique to determine foreign direct investment in Hungarian counties

Recent studies show regional factors play an important role to attract Foreign Direct Invest-ment (FDI) and, the performance of these factors varies within the country. Therefore, it isimportant to develop a measurement system to analyse the insight of these FDI factors. Inthis study, we used the regularization method with machine learning to get insight into theFDI determinants at the regional level. We used 18-years post-socialist period data at thecounty level from Hungary and applied a machine learning algorithm on different methodsof regressions such as a linear, ridge, lasso, and elastic net. We analyse the relation oftwo dependent variables, the total amount of FDI inflow in a county and disparity of FDIinflow in companies within the county, and used urbanization, GDP per capita, labourproductivity, market share of the companies, agglomeration of industries, and growth rateof the companies as predictors. Our results show that the elastic net is the best method todetermine the predictive performance of FDI at the regional level.