In this paper we test whether the key metals prices of gold and platinum significantly
improve inflation forecasts for the South African economy. We also test whether controlling
for conditional correlations in a dynamic setup, using bivariate Bayesian-Dynamic
Conditional Correlation (B-DCC) models, improves inflation forecasts. To achieve this we
compare out-of-sample forecast estimates of the B-DCC model to Random Walk,
Autoregressive and Bayesian VAR models. We find that for both the BVAR and BDCC models,
improving point forecasts of the Autoregressive model of inflation remains an elusive
exercise. This, we argue, is of less importance relative to the more informative density forecasts.
For this we find improved forecasts of inflation for the B-DCC models at all forecasting
horizons tested. We thus conclude that including metals price series as inputs to
inflation models leads to improved density forecasts, while controlling for the dynamic
relationship between the included price series and inflation similarly leads to significantly
improved density forecasts.