Abstract
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.