IDEAS home Printed from https://ideas.repec.org/a/rbs/ijbrss/v14y2025i6p512-527.html

Dating business cycle turning points for South Africa: A comparison of parametric and non-parametric dating methods

Author

Listed:
  • Malibongwe Nyathi

    (Tshwane University of Technology)

  • Simiso Msomi

    (University of KwaZulu Natal)

  • Ntokozo Nzimande

    (University of Johannesburg)

  • Mulatu Fekadu Zerihun

    (Tshwane University of Technology)

  • Besuthu Hlafa

    (Tshwane University of Technology)

  • Siyabonga Siboniso Mncube

    (Tshwane University of Technology)

  • Bhekithemba Khanyisani Mdlalose

    (Tshwane University of Technology)

  • Amara Liyabona Mngcutsha

    (Tshwane University of Technology)

Abstract

Several official dating institutions viz: SARB, NBER, OECD, CEPR, and others, provide Business Cycle (BC) chronologies with lags ranging from three months to several years. This is problematic for the various oppressed as it poses limits to the usefulness of BC indicators in terms of forecasting and policy implementation at large. In this article, we propose the construction of composite indicators as accurate measures of business cycles in South Africa. We use this composite index to facilitate comparison of both a non-parametric Bry and Boschan dating algorithm and a parametric Markov Switching dating method, in terms of performance and accuracy of dating business cycle turning points in South Africa. Utilising data spanning the period 2000M1 – 2018M12, empirical evidence obtained is such that, composite indices are better than single indicators due to information rich. Further, while the parametric and non-parametric methods’ performances are matched in terms of the number of turning points identified, however, the non-parametric method is more accurate in identifying these turning points. Due to its accuracy the non-parametric method proved to be a promising method of dating business cycle turning points in South Africa and alleviate the problems which are currently facing the South African Reserve Bank, the private sector and the economy at large. Key Words:Business Cycles, Markov Switching, Dynamic Factor Modeling, Principal Component, Bry and Boschan Algorithm

Suggested Citation

  • Malibongwe Nyathi & Simiso Msomi & Ntokozo Nzimande & Mulatu Fekadu Zerihun & Besuthu Hlafa & Siyabonga Siboniso Mncube & Bhekithemba Khanyisani Mdlalose & Amara Liyabona Mngcutsha, 2025. "Dating business cycle turning points for South Africa: A comparison of parametric and non-parametric dating methods," International Journal of Research in Business and Social Science (2147-4478), Center for the Strategic Studies in Business and Finance, vol. 14(6), pages 512-527, August.
    • Handle: RePEc:rbs:ijbrss:v:14:y:2025:i:6:p:512-527
    DOI: 10.20525/ijrbs.v14i6.4277
    as

    Download full text from publisher

    File URL: https://ssbfnet.com/ojs/index.php/ijrbs/article/view/4277/2958
    Download Restriction: no
    File URL: https://doi.org/10.20525/ijrbs.v14i6.4277
    Download Restriction: no
    File URL: https://libkey.io/10.20525/ijrbs.v14i6.4277?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. Yong Ma & Jinglan Zhang, 2016. "Financial Cycle, Business Cycle and Monetary Policy: Evidence from Four Major Economies," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 21(4), pages 502-527, October.
    2. Konstantin A. Kholodilin, 2002. "Two Alternative Approaches to Modelling the Nonlinear Dynamics of the Composite Economic Indicator," Economics Bulletin, AccessEcon, vol. 3(26), pages 1-18.
    3. Konstantin A. KHOLODILIN, 2002. "Unobserved Leading and Coincident Common Factors in the Post-War U.S. Business Cycle," LIDAM Discussion Papers IRES 2002008, Université catholique de Louvain, Institut de Recherches Economiques et Sociales (IRES).
    4. repec:ebl:ecbull:v:3:y:2002:i:25:p:1-17 is not listed on IDEAS
    5. Cyrille Lenoel & Garry Young, 2020. "Real-time turning point indicators: Review of current international practices," Economic Statistics Centre of Excellence (ESCoE) Discussion Papers ESCoE DP-2020-05, Economic Statistics Centre of Excellence (ESCoE).
    6. Harding, Don & Pagan, Adrian, 2002. "Dissecting the cycle: a methodological investigation," Journal of Monetary Economics, Elsevier, vol. 49(2), pages 365-381, March.
    7. Mr. Ivo Krznar & Mr. Troy D Matheson, 2017. "Financial and Business Cycles in Brazil," IMF Working Papers 2017/012, International Monetary Fund.
    8. repec:rza:wpaper:267 is not listed on IDEAS
    9. Hamilton, James D, 1989. "A New Approach to the Economic Analysis of Nonstationary Time Series and the Business Cycle," Econometrica, Econometric Society, vol. 57(2), pages 357-384, March.
    10. Chauvet, Marcelle, 1998. "An Econometric Characterization of Business Cycle Dynamics with Factor Structure and Regime Switching," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 39(4), pages 969-996, November.
    11. Adél Bosch & Franz Ruch, 2013. "An Alternative Business Cycle Dating Procedure for South Africa," South African Journal of Economics, Economic Society of South Africa, vol. 81(4), pages 491-516, December.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Konstantin A. Kholodilin, 2006. "Using the Dynamic Bi-Factor Model with Markov Switching to Predict the Cyclical Turns in the Large European Economies," Discussion Papers of DIW Berlin 554, DIW Berlin, German Institute for Economic Research.
    2. Carstensen, Kai & Heinrich, Markus & Reif, Magnus & Wolters, Maik H., 2020. "Predicting ordinary and severe recessions with a three-state Markov-switching dynamic factor model," International Journal of Forecasting, Elsevier, vol. 36(3), pages 829-850.
    3. Aastveit, Knut Are & Jore, Anne Sofie & Ravazzolo, Francesco, 2016. "Identification and real-time forecasting of Norwegian business cycles," International Journal of Forecasting, Elsevier, vol. 32(2), pages 283-292.
    4. Aastveit, Knut Are & Anundsen, André K. & Herstad, Eyo I., 2019. "Residential investment and recession predictability," International Journal of Forecasting, Elsevier, vol. 35(4), pages 1790-1799.
    5. Antonin Aviat & Frédérique Bec & Claude Diebolt & Catherine Doz & Denis Ferrand & Laurent Ferrara & Eric Heyer & Valérie Mignon & Pierre-Alain Pionnier, 2021. "Dating business cycles in France: a reference chronology," Sciences Po Economics Publications (main) hal-03373425, HAL.
    6. Olivier Darné & Laurent Ferrara, 2011. "Identification of Slowdowns and Accelerations for the Euro Area Economy," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 73(3), pages 335-364, June.
    7. Adrian Pagan, 2001. "The Getting of Macroeconomic Wisdom," International Economic Association Series, in: Jacques Drèze (ed.), Advances in Macroeconomic Theory, chapter 11, pages 219-235, Palgrave Macmillan.
    8. Vincent, BODART & Konstantin, KHOLODILIN & Fati, SHADMAN-MEHTA, 2005. "Identifying and Forecasting the Turning Points of the Belgian Business Cycle with Regime-Switching and Logit Models," Discussion Papers (ECON - Département des Sciences Economiques) 2005006, Université catholique de Louvain, Département des Sciences Economiques.
    9. Nissilä, Wilma, 2020. "Probit based time series models in recession forecasting – A survey with an empirical illustration for Finland," BoF Economics Review 7/2020, Bank of Finland.
    10. Issler, Joao Victor & Notini, Hilton & Rodrigues, Claudia & Soares, Ana Flávia, 2013. "Constructing coincident indices of economic activity for the Latin American economy," Revista Brasileira de Economia - RBE, EPGE Brazilian School of Economics and Finance - FGV EPGE (Brazil), vol. 67(1), April.
    11. Louise Holm, 2016. "The Swedish business cycle, 1969-2013," OECD Journal: Journal of Business Cycle Measurement and Analysis, OECD Publishing, Centre for International Research on Economic Tendency Surveys, vol. 2015(2), pages 1-22.
    12. Vincent, BODART & Konstantin A., KHOLODILIN & Fati, SHADMAN-MEHTA, 2003. "Dating and Forecasting the Belgian Business Cycle," LIDAM Discussion Papers IRES 2003018, Université catholique de Louvain, Institut de Recherches Economiques et Sociales (IRES).
    13. Arabinda Basistha, "undated". "Estimates of Quarterly and Monthly Episodes of Global Recessions: Evidence from Markov-switching Dynamic Factor Models," Working Papers 24-07, Department of Economics, West Virginia University.
    14. Sungyup Chung, 2016. "Assessing the regional business cycle asymmetry in a multi-level structure framework: a study of the top 20 US MSAs," The Annals of Regional Science, Springer;Western Regional Science Association, vol. 56(1), pages 229-252, January.
    15. repec:fgv:epgrbe:v:67:n:1:a:4 is not listed on IDEAS
    16. Howard J. Wall, 2007. "Regional business cycle phases in Japan," Review, Federal Reserve Bank of St. Louis, vol. 89(Jan), pages 61-80.
    17. Thierry Aimar & Francis Bismans & Claude Diebolt, 2012. "Economic Cycles: A Synthesis," Working Papers 12-11, Association Française de Cliométrie (AFC).
    18. Knut Are Aastveit & Anne Sofie Jore & Francesco Ravazzolo, 2014. "Forecasting recessions in real time," Working Paper 2014/02, Norges Bank.
    19. Kholodilin Konstantin A., 2005. "Forecasting the German Cyclical Turning Points: Dynamic Bi-Factor Model with Markov Switching," Journal of Economics and Statistics (Jahrbuecher fuer Nationaloekonomie und Statistik), De Gruyter, vol. 225(6), pages 653-674, December.
    20. Konstantin A. Kholodilin, 2005. "Forecasting the Turns of German Business Cycle: Dynamic Bi-factor Model with Markov Switching," Discussion Papers of DIW Berlin 494, DIW Berlin, German Institute for Economic Research.
    21. Kurowski, Łukasz & Rogowicz, Karol, 2018. "Are business and credit cycles synchronised internally or externally?," Economic Modelling, Elsevier, vol. 74(C), pages 124-141.

    More about this item

    Keywords

    ;
    ;
    ;
    ;
    ;

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:rbs:ijbrss:v:14:y:2025:i:6:p:512-527. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Umit Hacioglu (email available below). General contact details of provider: https://edirc.repec.org/data/ssbffea.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.