Bahramian, PejmanBalcilar, MehmetGupta, RanganKanda, Patrick T.2016-03-152016-01Patrick T. Kanda, Mehmet Balcilar, Pejman Bahramian & Rangan Gupta (2016) Forecasting South African inflation using non-linearmodels: a weighted loss-based evaluation, Applied Economics, 48:26, 2412-2427, DOI:10.1080/00036846.2015.1122731.0003-6846 (print)1466-4283 (online)10.1080/00036846.2015.1122731http://hdl.handle.net/2263/51890The 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.en© 2016 Taylor and Francis. This is an electronic version of an article published in Applied Economics, vol. 48, no. 26, pp. 2412-2427, 2016. doi :10.1080/00036846.2015.1122731. Applied Economics is available online at : http://www.tandfonline.comloi/raec20.InflationForecastingNon-linear modelsWeighted loss functionSouth Africa (SA)Postprint Article