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

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dc.contributor.advisor Sigauke, C.
dc.contributor.advisor Bere, A.
dc.contributor.author Netshivhazwaulu, Nyawedzeni
dc.date 2018
dc.date.accessioned 2019-05-28T12:25:13Z
dc.date.available 2019-05-28T12:25:13Z
dc.date.issued 2019-05-16
dc.identifier.citation Netshivhazwaulu, Nyawedzeni (2018) Forecasting Foreign Direct Investment in South Africa using Non-Parametric Quantile Regression Models, University of Venda, South Africa,<http://hdl.handle.net/11602/1297>.
dc.identifier.uri http://hdl.handle.net/11602/1297
dc.description MSc (Statistics) en_US
dc.description Department of Statistics
dc.description.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. en_US
dc.description.sponsorship NRF en_US
dc.format.extent 1 online resource (xii, 91 leaves: illustration (some color)
dc.language.iso en en_US
dc.rights University of Venda
dc.subject Foreign direct investment en_US
dc.subject Least absolute en_US
dc.subject Shrinkage and selection operator en_US
dc.subject Non-parametric quantile regression en_US
dc.subject Local linear kernel en_US
dc.subject.ddc 332.6730968
dc.subject.lcsh Investments -- South Africa
dc.subject.lcsh Investments, Foreign -- South Africa
dc.subject.lcsh Economics -- South Africa
dc.subject.lcsh South Africa -- Economic policy
dc.title Forecasting Foreign Direct Investment in South Africa using Non-Parametric Quantile Regression Models en_US
dc.type Dissertation en_US

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