Forecasting South African inflation using non-linearmodels: a weighted loss-based evaluation |
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Authors: | Patrick T. Kanda Mehmet Balcilar Pejman Bahramian |
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Affiliation: | 1. Department of Economics, University of Pretoria, Pretoria, South Africa;2. Department of Economics, Eastern Mediterranean University, Famagusta, Turkish Republic of Northern Cyprus, Turkey;3. Department of Economics, Eastern Mediterranean University, Famagusta, Turkish Republic of Northern Cyprus, Turkey |
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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. |
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Keywords: | Inflation forecasting non-linear models weighted loss function South Africa |
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