Abstract:
The conduct of inflation targeting is heavily dependent on accurate inflation forecasts. Non-linear
models have increasingly featured, along with linear counterparts, in the forecasting literature. In
this study, we focus on forecasting South African inflation by means of non-linear models and
using a long historical dataset of seasonally adjusted monthly inflation rates spanning from
1921:02 to 2013:01. For an emerging market economy such as South Africa, non-linearities can
be a salient feature of such long data, hence the relevance of evaluating non-linear models’
forecast performance. In the same vein, given the fact that 1969:10 marks the beginning of a
protracted rising trend in South African inflation data, we estimate the models for an in-sample
period of 1921:02–1966:09 and evaluate 1, 4, 12, and 24 step-ahead forecasts over an out-ofsample
period of 1966:10–2013:01. In addition, using a weighted loss function specification, we
evaluate the forecast performance of different non-linear models across various extreme economic
environments and forecast horizons. In general, we find that no competing model
consistently and significantly beats the LoLiMoT’s performance in forecasting South African
inflation.
