Abstract:
Foreign 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.
