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Measuring Core Inflation in South Africa

Author

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  • Stan Plessis
  • Gideon Rand
  • Kevin Kotzé

Abstract

Measures of core inflation convey critical information about an economy. They have a direct effect on the policymaking process, particularly in inflation-targeting countries, and are utilised in forecasting and modelling exercises. In South Africa, the price indices on which inflation is based have been subject to important structural breaks following changes to the underlying basket of goods and the methodology for constructing price indices. This paper seeks to identify a consistent measure of core inflation for South Africa using trimmed means estimates, measures that exclude changes in food and energy prices, dynamic factor models, and wavelet decompositions. After considering the forecasting ability of these measures, which provide an indication of expected second-round inflationary effects, traditional in-sample criteria were used for further comparative purposes. The results suggest that wavelet decompositions provide a useful measure of this critical variable.

Suggested Citation

  • Stan Plessis & Gideon Rand & Kevin Kotzé, 2015. "Measuring Core Inflation in South Africa," South African Journal of Economics, Economic Society of South Africa, vol. 83(4), pages 527-548, December.
  • Handle: RePEc:bla:sajeco:v:83:y:2015:i:4:p:527-548
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    File URL: http://hdl.handle.net/10.1111/saje.12090
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    Cited by:

    1. Francis Leni Anguyo & Rangan Gupta & Kevin Kotzé, 2020. "Inflation dynamics in Uganda: a quantile regression approach," Macroeconomics and Finance in Emerging Market Economies, Taylor & Francis Journals, vol. 13(2), pages 161-187, May.
    2. Byron Botha & Rulof Burger & Kevin Kotzé & Neil Rankin & Daan Steenkamp, 2023. "Big data forecasting of South African inflation," Empirical Economics, Springer, vol. 65(1), pages 149-188, July.
    3. Mehmet Balcilar & Rangan Gupta & Kevin Kotzé, 2017. "Forecasting South African macroeconomic variables with a Markov-switching small open-economy dynamic stochastic general equilibrium model," Empirical Economics, Springer, vol. 53(1), pages 117-135, August.
    4. Franz Ruch & Mehmet Balcilar & Rangan Gupta & Mampho P. Modise, 2020. "Forecasting core inflation: the case of South Africa," Applied Economics, Taylor & Francis Journals, vol. 52(28), pages 3004-3022, June.
    5. Eliana R. González-Molano & Ramón Hernández-Ortega & Edgar Caicedo-García & Nicolás Martínez-Cortés & Jose Vicente Romero & Anderson Grajales-Olarte, 2020. "Nueva Clasificación del BANREP de la Canasta del IPC y revisión de las medidas de Inflación Básica en Colombia," Borradores de Economia 1122, Banco de la Republica de Colombia.
    6. Franz Ruch & Stan du Plessis, 2015. "SecondRound Effects from Food and Energy Prices an SBVAR approach," Working Papers 7008, South African Reserve Bank.
    7. Ladi R. Bala-Keffi & Donald G. Mbaka & Nuruddeen Usman, 2020. "Alternative Core Inflation Measures in Nigeria: An Examination," Applied Economics and Finance, Redfame publishing, vol. 7(4), pages 112-120, July.
    8. Murhula, Pacifique, 2020. "Tendance de l'inflation sous-jacente en RDC: une modélisation à partir de l'approche VAR structurelle [Trend of Core inflation in DRCongo: a model based on the Structural VAR approach]," MPRA Paper 105005, University Library of Munich, Germany, revised 08 Jan 2021.

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    More about this item

    JEL classification:

    • C43 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Index Numbers and Aggregation
    • E31 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Price Level; Inflation; Deflation

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