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Analysing South Africa's Inflation Persistence Using an ARFIMA Model with Markov-Switching Fractional Differencing Parameter

Author

Listed:
  • Mehmet Balcilar

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

  • Rangan Gupta

    (Department of Economics, University of Pretoria)

  • Charl Jooste

    (Department of Economics, University of Pretoria)

Abstract

We test the inertial properties of South African inflation in a Markov-Switching autoregressive fractionally integrated moving average model. This allows us to test for long memory and study the persistence of inflation in multiple regimes. We show that inflation is more volatile and persistent during high inflation episodes relative to low inflation episodes. We estimate that it takes approximately 70 months for 50 percent of the shocks to dissipate in a high inflation regime compared to 10 months in a low inflation regime.

Suggested Citation

  • Mehmet Balcilar & Rangan Gupta & Charl Jooste, 2014. "Analysing South Africa's Inflation Persistence Using an ARFIMA Model with Markov-Switching Fractional Differencing Parameter," Working Papers 201440, University of Pretoria, Department of Economics.
  • Handle: RePEc:pre:wpaper:201440
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    References listed on IDEAS

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    1. Stan du Plessis & Ben Smit & Rudi Steinbach, 2014. "A mediumsized open economy DSGE model of South Africa," Working Papers 6319, South African Reserve Bank.
    2. Serena Ng & Pierre Perron, 2002. "PPP May not Hold Afterall: A Further Investigation," Annals of Economics and Finance, Society for AEF, vol. 3(1), pages 43-64, May.
    3. Ruthira Naraidoo & Rangan Gupta, 2009. "Modelling monetary policy in South Africa: Focus on inflation targeting era using a simple learning rule," Working Papers 200904, University of Pretoria, Department of Economics.
    4. Mehmet Balcilar, 2004. "Persistence in Inflation: Does Aggregation Cause Long Memory?," Emerging Markets Finance and Trade, Taylor & Francis Journals, vol. 40(5), pages 25-56, September.
    5. Ehrmann, Michael & Ellison, Martin & Valla, Natacha, 2003. "Regime-dependent impulse response functions in a Markov-switching vector autoregression model," Economics Letters, Elsevier, vol. 78(3), pages 295-299, March.
    6. Logan Rangasamy, 2009. "Inflation Persistence And Core Inflation: The Case Of South Africa," South African Journal of Economics, Economic Society of South Africa, vol. 77(3), pages 430-444, September.
    7. Haldrup, Niels & Nielsen, Morten Orregaard, 2006. "A regime switching long memory model for electricity prices," Journal of Econometrics, Elsevier, vol. 135(1-2), pages 349-376.
    8. Rangan Gupta & Josine Uwilingiye, 2012. "Comparing South African Inflation Volatility Across Monetary Policy Regimes: An Application of Saphe Cracking," Journal of Developing Areas, Tennessee State University, College of Business, vol. 46(1), pages 45-54, January-J.
    9. Haldrup Niels & Nielsen Morten Ø., 2006. "Directional Congestion and Regime Switching in a Long Memory Model for Electricity Prices," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 10(3), pages 1-24, September.
    10. Massimiliano Caporin & Juliusz Preś, 2013. "Forecasting Temperature Indices Density with Time‐Varying Long‐Memory Models," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 32(4), pages 339-352, July.
    11. Zelda Blignaut & Greg Farrell & Victor Munyama & Logan Rangasamy, 2009. "A Note On The Trimmed Mean Measure Of Core Inflation In South Africa," South African Journal of Economics, Economic Society of South Africa, vol. 77(4), pages 538-552, December.
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    Citations

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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. Petrevski, Goran, 2023. "Macroeconomic Effects of Inflation Targeting: A Survey of the Empirical Literature," EconStor Preprints 271122, ZBW - Leibniz Information Centre for Economics.
    3. Sakiru Adebola Solarin & Luis A. Gil-Alana & Carmen Lafuente, 2020. "Persistence of the Misery Index in African Countries," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 147(3), pages 825-841, February.
    4. Massimiliano Caporin & Rangan Gupta, 2017. "Time-varying persistence in US inflation," Empirical Economics, Springer, vol. 53(2), pages 423-439, September.
    5. Phiri, Andrew, 2017. "Inflation persistence in BRICS countries: A quantile autoregressive (QAR) model," MPRA Paper 79956, University Library of Munich, Germany.
    6. Andrew Phiri, 2018. "Inflation persistence in BRICS countries: A quantile autoregressive (QAR) approach," Business and Economic Horizons (BEH), Prague Development Center, vol. 14(1), pages 97-104, January.
    7. Goran Petrevski, 2023. "Macroeconomic Effects of Inflation Targeting: A Survey of the Empirical Literature," Papers 2305.17474, arXiv.org.
    8. Gideon Du Rand & Kevin Kotze & Stan Du Plessis, 2015. "Measuring Core Inflation in South Africa," Working Papers 503, Economic Research Southern Africa.
    9. Ibrahim D. Raheem, 2018. "Inflation rate of 14–16% is fair for the sub-Saharan African dollarization," Journal of Economics and Finance, Springer;Academy of Economics and Finance, vol. 42(4), pages 779-794, October.
    10. 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.
    11. Fernando Zarzosa Valdivia, 2020. "Inflation Dynamics in the ABC (Argentina, Brazil and Chile) countries," Ensayos de Política Económica, Departamento de Investigación Francisco Valsecchi, Facultad de Ciencias Económicas, Pontificia Universidad Católica Argentina., vol. 3(2), pages 77-99, Octubre.
    12. Sihle Kubheka, 2023. "South African inflation modelling using bootstrapped long short-term memory methods," SN Business & Economics, Springer, vol. 3(7), pages 1-11, July.

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    More about this item

    Keywords

    Inflation persistence; MS-ARFIMA; inflation regimes;
    All these keywords.

    JEL classification:

    • E31 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Price Level; Inflation; Deflation
    • C20 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - General

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