Forecasting South African inflation using non-linearmodels: a weighted loss-based evaluation

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-of-sample 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.

KEYWORDS:

• Inflation
• forecasting
• non-linear models
• weighted loss function
• South Africa

JEL CODES:

• C32
• E31
• E52

Acknowledgement

We would like to thank two anonymous referees for many helpful comments. However, any remaining errors are solely ours.

Disclosure statement

No potential conflict of interest was reported by the authors.

Notes

¹ Genetic Programming (GP) is related to but different from GA. The primary purpose of GP is to find computer programs for user-defined tasks. One application of GP is to find a general formula from a predefined set of functions that best approximates the relationship between an output and a set of inputs.