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Forecasting South African Inflation Using Non-Linear Models: A Weighted Loss-Based Evaluation

Author

Listed:
  • Pejman Bahramian

    () (Department of Economics, Eastern Mediterranean University, Famagusta, Turkish Republic of Northern Cyprus, via Mersin 10, Turkey)

  • Mehmet Balcilar

    () (Department of Economics, Eastern Mediterranean University, Famagusta, Northern Cyprus , via Mersin 10, Turkey)

  • Rangan Gupta

    () (Department of Economics, University of Pretoria)

  • Patrick T. kanda

    () (Department of Economics, University of Pretoria)

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 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.

Suggested Citation

  • Pejman Bahramian & Mehmet Balcilar & Rangan Gupta & Patrick T. kanda, 2014. "Forecasting South African Inflation Using Non-Linear Models: A Weighted Loss-Based Evaluation," Working Papers 15-19, Eastern Mediterranean University, Department of Economics.
  • Handle: RePEc:emu:wpaper:15-19.pdf
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    References listed on IDEAS

    as
    1. Rangan Gupta, 2006. "FORECASTING THE SOUTH AFRICAN ECONOMY WITH VARs AND VECMs," South African Journal of Economics, Economic Society of South Africa, vol. 74(4), pages 611-628, December.
    2. Geoffrey Woglom, 2005. "Forecasting South African Inflation," South African Journal of Economics, Economic Society of South Africa, vol. 73(2), pages 302-320, June.
    3. Rangan Gupta & Alain Kabundi, 2010. "Forecasting macroeconomic variables in a small open economy: a comparison between small- and large-scale models," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 29(1-2), pages 168-185.
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    5. Carstensen Kai & Wohlrabe Klaus & Ziegler Christina, 2011. "Predictive Ability of Business Cycle Indicators under Test: A Case Study for the Euro Area Industrial Production," Journal of Economics and Statistics (Jahrbuecher fuer Nationaloekonomie und Statistik), De Gruyter, vol. 231(1), pages 82-106, February.
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    11. Rangan Gupta & Rudi Steinbach, 2010. "Forecasting Key Macroeconomic Variables of the South African Economy: A Small Open Economy New Keynesian DSGE-VAR Model," Working Papers 201019, University of Pretoria, Department of Economics.
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    18. Jp Heever, 2001. "A Note On Inflation Targeting In South Africa," South African Journal of Economics, Economic Society of South Africa, vol. 69(1), pages 168-177, March.
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    1. repec:ipg:wpaper:2014-492 is not listed on IDEAS
    2. repec:ipg:wpaper:2014-474 is not listed on IDEAS
    3. repec:ipg:wpaper:2014-562 is not listed on IDEAS
    4. repec:ipg:wpaper:2014-516 is not listed on IDEAS
    5. repec:ipg:wpaper:2014-462 is not listed on IDEAS
    6. Franz Ruch & Mehmet Balcilar & Mampho P. Modise & Rangan Gupta, 2015. "Forecasting Core Inflation: The Case of South Africa," Working Papers 201543, University of Pretoria, Department of Economics.
    7. repec:ipg:wpaper:2014-468 is not listed on IDEAS
    8. repec:ipg:wpaper:2014-548 is not listed on IDEAS
    9. repec:ipg:wpaper:2014-475 is not listed on IDEAS

    More about this item

    Keywords

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

    JEL classification:

    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models
    • E31 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Price Level; Inflation; Deflation
    • E52 - Macroeconomics and Monetary Economics - - Monetary Policy, Central Banking, and the Supply of Money and Credit - - - Monetary Policy

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