Forecasting South African Ination Using Non-linear Models: A Weighted Loss-based Evaluation

The conduct of in ation targeting is heavily dependent on accurate in ation forecasts. Non-linear models have increasingly featured, along with linear counterparts, in the forecasting literature. In this study, we focus on forecasting South African in ation by means of non-linear models and using a long historical dataset of seasonally-adjusted monthly in ation 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 in ation data, we estimate the models for an in-sample period of 1921:02-1966:09 and evaluate 24 step-ahead forecasts over an out-of-sample period of 1966:10-2013:01. In addition, using a weighted loss function specication, we evaluate the forecast performance of dierent non-linear models across various extreme economic environments and forecast horizons. In general, we nd that no competing model consistently and signicantly beats the LoLiMoT's performance in forecasting South African in ation.