Forecasting economic downturns in South Africa using leading indicators and machine learning

We identify South African business cycles using the algorithm of Bry-Boschan and show that the identified turning points are very similar to those from other approaches. We demonstrate that South Africa has a very volatile business cycle that makes it particularly difficult to predict turning points in the economic cycle. South Africa’s business cycle is characterised by relatively long downswings and short upswing phases with low amplitude. We find that the South African Reserve Bank (SARB)’s Leading Indicator does not substantive improve predictions of the business cycle relative to GDP itself. We assess the performance of a range of potential leading indicators in identifying economic downturns and consider whether alternative indicators and estimation approaches can produce better predictions than those of the SARB. We demonstrate that using a larger information set produces substantially better business cycle predictions, especially when using machine learning techniques. Our findings have implications for the creation of composite leading indicators, with our results suggesting that many of the macroeconomic variables considered by analysts as leading indicators do not provide good signals of GDP growth or developments in the South African business cycle.