Abstract
Characterising the SA business cycle, it is fair to conclude that it has been shaped by both discrete structural change (and exogenous shocks) and its inherent regularity (i.e. endogenous cyclical forces). Being a small and open economy, the SA business cycle is sensitive to global influences, via both the trade and financial channels. Often non-economic factors gave rise to the so-called ‘stop-go’ business cycles of the 1970s and 1980s (and first half of the 1990s). However, through all these tumultuous times a certain export and fixed investment-centred business cycle momentum persisted, exerting itself more fully during the 1990s in response to the political change and macro-economic policy improvements. Unfortunately, SA’s longest post-war business cycle expansion was aborted due to the impact of the Great Recession in 2009 and its aftermath. This latter-mentioned period of unconventional monetary policies in the major advanced economies has witnessed heightened financial volatility and poor domestic real economic growth around a 2% tempo (2012–2014). Suffice to note that the two post-apartheid recessions (1997–1999 and 2009) have been relatively mild affairs and the two expansion phases (1999–2007 and 2009–2013) exceptionally long in a historical context.
While the discrete structural changes and exogenous shocks render economic forecasting hazardous, this does not imply the endogenous regularity of the business cycle has been suspended. Economic forecasters have little option, but to continue applying their trade. Pondering the future of economic forecasting, econometric models provide the best long-run hope for successful forecasting; however, suitable methods need to be developed in order to improve models’ robustness to unanticipated structural breaks. The indicator approach to economic forecasting provides fertile ground in compensating for this weakness. While the economic forecaster needs to tread carefully beyond a forecast horizon of more than 1 year, sound interaction between science and evidence in the forecasting process, good judgement combined with sound econometric modelling, teamwork and the contemplation of alternative scenarios are all elements of value addition in economic forecasting. Provided these elements, the demand for economic forecasting services is likely to continue to grow.
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Notes
- 1.
A third school can be identified, namely, the structuralist—or institutionalist—school, who believes business cycles are random events dictated by structural and institutional change (see Mohr 2015: 412).
- 2.
This is mirrored in the fact that during 1993–1995 minerals contributed 17.5% of merchandise exports, increasing to 26.9% during 2011–2013 (McCarthy 2015: 9).
- 3.
Since 1970, the South African Reserve Bank regularly determines the upper and lower turning points of the domestic business cycle using extensive statistical and econometric techniques (see Venter 2018).
- 4.
This is the average duration of 14 post-WWII economic expansion phases up to the end of 1996, i.e. excluding the 1999–2007 and 2009 to date expansion phases.
- 5.
In a US survey of forecasting methods applied by forecasters conducted in the early 1980s, the American Statistical Association (ASA), working in conjunction with the National Bureau for Economic Research (NBER), found that an informal GNP model served as the most popular technique (74% of the respondents using this method, with 56% rating it as the most useful); second in line was econometric models (53% and 13%, respectively); third was leading indicators (49% and 12%) and fourth, anticipation surveys (42% and 1%)—see Zarnowitz (1992: 402). This usage pattern may have changed as computing technology developed over the years. In a 1987 survey by the National Association of Business Economists (NABE), it was found that on average the contribution of econometric models to the forecasting outcome was 60%, judgement around 30% and time series methods, current data analysis and interaction with others around 15% (Zarnowitz 1992: 405).
- 6.
Koopmans’ critique (1947) had its origins in the ideas of the celebrated philosopher, Sir Karl Popper, who published his The Logic of Scientific Discovery in 1934. He stated that science can only advance through apriori theories capable of being rejected through empirical testing. Whereas theories had to be falsifiable, Achuthan and Banerji point out that the Burns and Mitchell approach to understanding economic turning points relied on descriptive observations that were not ‘falsifiable’ like mathematical equations (2004: 25–26) and hence Koopmans’ and others’ (notably Paul Samuelson’s) critique of the ‘indicator approach’.
- 7.
Achuthan and Banerji currently lead the Economic Cycle Research Institute (ECRI) in the USA having continued the work of Geoffrey Moore, originally employed by the NBER, but leaving that institute when it became clear that it moved away from its dedicated business cycle research origins in the 1970s. Moore is well-known for the development of composite leading, coinciding and lagging business cycle indicators for the USA and many other countries. Even the American scientific community acknowledged his work during the 1970s (see Achuthan and Banerji 2004: 33).
- 8.
For a comprehensive treatment of this topic, i.e. surveys on forecasting methods and accuracy in the 1970s and 1980s, see Zarnowitz (1992: 389–413). These findings tend to agree with the results of a global survey of model usage conducted recently by the Swedish National Institute of Economic Research (NIER) (Hjelm et al. 2015). Institutions adopt different models and methods depending on their respective needs, and it is difficult to conclude that one particular method/model is superior all the time.
- 9.
The IMF is developing a global modelling structure, called the Flexible System of Global Models (FSGM). The model consists of three core modules, each containing 24 blocks for countries and regions. Household consumption and business fixed investment decisions are modelled from micro-economic foundations similar to that in DSGE modelling, but elements such as trade, labour supply and inflation have reduced form representations. The IMF describes each FSGM module as ‘an annual, multi-region, general equilibrium model of the global economy combining both micro-founded and reduced-form formulations of various economic sectors’ (Andrle et al. 2015: 5).
- 10.
Prof CGW Schumann, who helped in establishing the BER, published a remarkable book in 1938 on business cycles in South Africa over the period 1806–1936, titled: Structural Changes and Business Cycles in South Africa, 1806–1936.
- 11.
These developments occurred at a time when econometric modelling and a so-called scientific approach to economics came into vogue. For instance, at more or less the same time, Achuthan and Banerji describe in their book (2004: 32–34) how the US National Bureau for Economic Research (NBER) ‘veered off its sixty-year path of dedicated business cycle research’ labelled by some as ‘measurement without theory’ by shifting the institutes’ focus to a more ‘scientific’ approach. The roots of this shift lie further back (end of the WWII), with the attack by the mainstream economics community on dedicated business cycle research as being ‘unscientific’.
- 12.
The application of cointegration techniques was initially done using a two-step Engle-Granger method, but later refined by Linette Ellis and Ben Smit (after the publication of the Bank of England’s Macro-econometric Model in 1999) using one-step, or so-called autoregressive distributed lag (ARDL) cointegration techniques.
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Laubscher, P. (2019). Forecasting Business Cycles in South Africa. In: Smirnov, S., Ozyildirim, A., Picchetti, P. (eds) Business Cycles in BRICS. Societies and Political Orders in Transition. Springer, Cham. https://doi.org/10.1007/978-3-319-90017-9_28
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