Forecasting Foreign Direct Investment in South Africa using Non-Parametric Quantile Regression Models

MSc (Statistics)Department of StatisticsForeign direct investment plays an important role in the economic growth process in the host country, since foreign direct investment is considered as a vehicle transferring new ideas, capital, superior technology and skills from developed country to developing country. Non-parametric quantile regression is used in this study to estimate the relationship between foreign direct investment and the factors in uencing it in South Africa, using the data for the period 1996 to 2015. The variables are selected using the least absolute shrinkage and selection operator technique, and all the variables were selected to be in the models. The developed non-parametric quantile regression models were used for forecasting the future in ow of foreign direct investment in South Africa. The forecast evaluation was done for all models and the laplace radial basis kernel, ANOVA radial basis kernel and linear quantile regression averaging were selected as the three best models based on the accuracy measures (mean absolute percentage error, root mean square error and mean absolute error). The best set of forecast was selected based on the prediction interval coverage probability, Prediction interval normalized average deviation and prediction interval normalized average width. The results showed that linear quantile regression averaging is the best model to predict foreign direct investment since it had 100% coverage of the predictions. Linear quantile regression averaging was also con rmed to be the best model under the forecast error distribution. One of the contributions of this study was to bring the accurate foreign direct investment forecast results that can help policy makers to come up with good policies and suitable strategic plans to promote foreign direct investment in ows into South Africa.NR

Prediction of Foreign Direct Investment: an Application to South African Data

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