Forecasting South African Inflation Using Non-Linear Models: A Weighted Loss-Based Evaluation
Pejman Bahramian (),
Rangan Gupta and
Patrick T. kanda ()
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Patrick T. kanda: Department of Economics, University of Pretoria
No 15-19, Working Papers from Eastern Mediterranean University, Department of Economics
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 infl ation 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 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 (search for similar items in EconPapers)
JEL-codes: C32 E31 E52 (search for similar items in EconPapers)
Pages: 19 pages
New Economics Papers: this item is included in nep-for, nep-ger, nep-mac and nep-mon
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http://repec.economics.emu.edu.tr/RePEc/emu/wpaper/15-19.pdf First version, 2014 (application/pdf)
Journal Article: Forecasting South African inflation using non-linearmodels: a weighted loss-based evaluation (2016)
Working Paper: Forecasting South African Inflation Using Non-Linear Models: A Weighted Loss-Based Evaluation (2014)
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