Testing the out-of-sample forecasting ability of a financial conditions index for South Africa

Abstract

The importance of financial instability for the world economy has been severely demonstrated since the 2007–8 global financial crisis, highlighting the need for a better understanding of financial conditions. We consider a financial conditions index (FCI) for South Africa that is constructed from sixteen financial variables and test whether the FCI does better than its individual financial components in forecasting the key macroeconomic variables of output growth, inflation, and interest rates. Two sets of out-of-sample forecasts are obtained—one from a benchmark autoregressive (AR) model and one from a nested autoregressive distributed lag (ARDL) model that includes one financial variable at a time. This concept of forecast encompassing is used to examine the out-of-sample forecasting ability of these financial variables as well as of the FCI, while also controlling for data mining.