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

Simple item page
dc.contributor.advisorSigauke, C.
dc.contributor.advisorBere, A.
dc.contributor.authorNetshivhazwaulu, Nyawedzeni
dc.date2018
dc.date.accessioned2019-05-28T12:25:13Z
dc.date.available2019-05-28T12:25:13Z
dc.date.issued2019-05-16
dc.descriptionMSc (Statistics)en_US
dc.descriptionDepartment of Statistics
dc.description.abstractForeign 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.sponsorshipNRFen_US
dc.format.extent1 online resource (xii, 91 leaves: illustration (some color)
dc.identifier.apacitationNetshivhazwaulu, N. (2019). <i>Forecasting Foreign Direct Investment in South Africa using Non-Parametric Quantile Regression Models</i>. (). . Retrieved from http://hdl.handle.net/11602/1297en_ZA
dc.identifier.chicagocitationNetshivhazwaulu, Nyawedzeni. <i>"Forecasting Foreign Direct Investment in South Africa using Non-Parametric Quantile Regression Models."</i> ., , 2019. http://hdl.handle.net/11602/1297en_ZA
dc.identifier.citationNetshivhazwaulu, 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.ris TY - Dissertation AU - Netshivhazwaulu, Nyawedzeni AB - 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. DA - 2019-05-16 DB - ResearchSpace DP - Univen KW - Foreign direct investment KW - Least absolute KW - Shrinkage and selection operator KW - Non-parametric quantile regression KW - Local linear kernel LK - https://univendspace.univen.ac.za PY - 2019 T1 - Forecasting Foreign Direct Investment in South Africa using Non-Parametric Quantile Regression Models TI - Forecasting Foreign Direct Investment in South Africa using Non-Parametric Quantile Regression Models UR - http://hdl.handle.net/11602/1297 ER - en_ZA
dc.identifier.urihttp://hdl.handle.net/11602/1297
dc.identifier.vancouvercitationNetshivhazwaulu N. Forecasting Foreign Direct Investment in South Africa using Non-Parametric Quantile Regression Models. []. , 2019 [cited yyyy month dd]. Available from: http://hdl.handle.net/11602/1297en_ZA
dc.language.isoenen_US
dc.rightsUniversity of Venda
dc.subjectForeign direct investmenten_US
dc.subjectLeast absoluteen_US
dc.subjectShrinkage and selection operatoren_US
dc.subjectNon-parametric quantile regressionen_US
dc.subjectLocal linear kernelen_US
dc.subject.ddc332.6730968
dc.subject.lcshInvestments -- South Africa
dc.subject.lcshInvestments, Foreign -- South Africa
dc.subject.lcshEconomics -- South Africa
dc.subject.lcshSouth Africa -- Economic policy
dc.titleForecasting Foreign Direct Investment in South Africa using Non-Parametric Quantile Regression Modelsen_US
dc.typeDissertationen_US
Files
Original bundle
Now showing 1 - 1 of 1
Thumbnail Image
Name:
Dissertation - Netshivhazwaulu, n.-.pdf
Size:
551.07 KB
Format:
Adobe Portable Document Format
Description:
Download
License bundle
Now showing 1 - 1 of 1
No Thumbnail Available
Name:
license.txt
Size:
1.71 KB
Format:
Item-specific license agreed upon to submission
Description:
Download
Collections
Theses and Dissertations
Theses and Dissertations