M.Com. (Financial Economics) / Forecasting inflation is an important concern for economists and business alike throughout the world. Despite the relative success of macroeconomic forecasting models in forecasting inflation, there is potential to improve these models to account for nonlinear relationships between inflation and the chosen independent variables. Artificial neural networks (ANNs) have found increased applicability as a potential nonlinear forecasting tool that accounts for nonlinearity found in data. In this study, we investigate the ability of genetically optimised neural networks to forecast South African inflation. The results were compared to economic forecasts obtained from traditional econometric models as well as macroeconomic structural models. The results obtained show that the genetically optimised neural networks indicate some ability to be used as potential forecasting tools. Their biggest advantage over the traditional forecasting techniques is that they do not impose the restriction of linearity on the data to be forecasted.
| Identifer | oai:union.ndltd.org:netd.ac.za/oai:union.ndltd.org:uj/uj:4219 |
| Date | 03 March 2014 |
| Source Sets | South African National ETD Portal |
| Detected Language | English |
| Type | Thesis |
| Rights | University of Johannesburg |
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