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

Malibongwe Nyathi, Simiso Msomi, Ntokozo Nzimande, Mulatu Fekadu Zerihun, Besuthu Hlafa, Siyabonga Siboniso Mncube, Bhekithemba Khanyisani Mdlalose and Amara Liyabona Mngcutsha
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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

International Journal of Research in Business and Social Science (2147-4478), 2025, vol. 14, issue 6, 512-527

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

Date: 2025
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