Journal of Statistics Applications & Probability


Foreign direct investment is considered as a vehicle for transferring new ideas, capital, superior technology and skills from developed countries to developing countries. Kernel quantile regression is used in this study to estimate the relationship between foreign direct investment and the factors influencing it in South Africa, using data for the period 1996 to 2015. Using the least absolute shrinkage and selection operator technique, all the variables were selected to be in the models. The developed kernel quantile regression models were used for forecasting the future inflow of foreign direct investment in South Africa. The forecast evaluation was done on all the models and the model based on the ANOVA radial basis kernel was selected as the best in terms of the accuracy measures (mean absolute percentage error, root mean square error and mean absolute error). The forecasts from the individual models were then combined using linear quantile regression averaging. The kernel quantile regression model using an ANOVA radial basis kernel was found to be the best model for forecasting foreign direct investment in South Africa. Accurate forecasts of FDI aid in economic planning. Identification of key drivers of FDI inflow can assist in crafting strategies to attract more FDI.

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