In this paper, we use high dimensional models, estimated by the least absolute shrinkage and selection operator (LASSO) to forecast Brazilian inflation. The models are compared to benchmark specifications such as linear autoregressive (AR) and the factor models based on principal components. Our results show that the LASSO-based specifications have the smallest errors for short-horizon forecasts. However, for long horizons, the AR benchmark is the best model with respect to point forecasts, even though there is no significant difference between them. The factor model also produces some good long-horizon forecasts in a few cases. We estimated all the models for the two most important Brazilian inflation measures, the IPCA and the IGP-M indexes. The results also show that there are differences in the selected variables for both measures. Finally, the most important variables selected by the LASSO-based models are, in general, related to government debt and money. On the other hand, variables such as unemployment and production are rarely selected by the LASSO. Therefore, our evidence is against the Phillips curve as the driving mechanism of Brazilian inflation.