In this article we compare the accuracy on nowcasts obtained with different models and different sets of indicators used as predictors for a set of 19 major economies. We compare the performance of mixed-frequency Bayesian VAR models, Dynamic Factor models and unrestricted MIDAS models with L1 regularization. We test different groups of commodity prices as possible predictors: energy indicators, agricultural commodities, precious metals and industrial metals prices. We find that among all the indicator groups tested energy commodities prices yield the highest average nowcasting accuracy, even though the accuracy of models utilizing all the indicators available remains slightly higher. Among all the models tested, the highest quality is yielded by Mixed-Frequency Bayesian VAR models. We also emphasize the importance of manual selection of predictors for non-diversified economies, where it can significantly improve the accuracy of nowcasts compared to the models with a wide set of predictors.