The price of gold is influenced by a wide range of local and global factors such as commodity prices, interest rates, inflation expectations, exchange rate changes and stock market volatility among others. Hence, forecasting the price of gold is a notoriously diffi cult task and the main problem a researcher faces is to select the relevant regressors at each point in time. This combination of model and parameter uncertainty is explicitly accounted for by Dynamic Model Averaging which allows both the forecasting model and the coefficients to change over time. Based on this framework, we systematically evaluate a large set of possible gold price determinants and use both the predictive likelihood and the mean squared error as a measure of the forecasting performance. We carefully assess which predictors are relevant for forecasting at different points in time through the posterior probability. Our findings show that (1) DMA improves forecasts compared to other frameworks and (2) provides clear evidence for the timevariation of gold price predictors.