The objectives of the research study are to review existing subnational credit rating methodologies
and their applicability in the South African context, to develop the quantitative parts of credit
rating methodologies for two provincial departments (Department of Health and Department of
Education) that best predict future payment behaviour, to test the appropriateness of the proposed
methodologies and to construct the datasets needed.
The literature study includes background information regarding the uniqueness of South Africa’s
provinces and credit rating methodologies in general. This is followed by information on subnational
credit rating methodologies, including a review of existing subnational credit rating methodologies
and an assessment of the applicability of the information provided in the South African context.
Lastly, the applicable laws and regulations within the South African regulatory framework are provided.
The knowledge gained from the literature study is applied to the data that have been collected
to predict the two departments’ future payment behaviour. Linear regression modelling is used
to identify the factors that best predict future payment behaviour and to assign weights to the
identified factors in a scientific manner. The resulting payment behaviour models can be viewed as
the quantitative part of the credit ratings. This is followed by a discussion on further investigations
to improve the models.
The developed models (both the simple and the advanced models) are tested with regard to prediction
accuracies using RAG (Red, Amber or Green) statuses. This is followed by recommendations
regarding future model usage that conclude that the department-specific models outperform the
generic models in terms of prediction accuracies. / PhD (Risk analysis), North-West University, Potchefstroom Campus, 2015
| Identifer | oai:union.ndltd.org:netd.ac.za/oai:union.ndltd.org:nwu/oai:dspace.nwu.ac.za:10394/15156 |
| Date | January 2015 |
| Creators | Fourie, Erika |
| Source Sets | South African National ETD Portal |
| Language | English |
| Detected Language | English |
| Type | Thesis |
Page generated in 0.0026 seconds