  This paper examines the effectiveness of several forecasting methods for
predicting inflation, focusing on aggregating disaggregated forecasts - also
known in the literature as the bottom-up approach. Taking the Brazilian case as
an application, we consider different disaggregation levels for inflation and
employ a range of traditional time series techniques as well as linear and
nonlinear machine learning (ML) models to deal with a larger number of
predictors. For many forecast horizons, the aggregation of disaggregated
forecasts performs just as well survey-based expectations and models that
generate forecasts using the aggregate directly. Overall, ML methods outperform
traditional time series models in predictive accuracy, with outstanding
performance in forecasting disaggregates. Our results reinforce the benefits of
using models in a data-rich environment for inflation forecasting, including
aggregating disaggregated forecasts from ML techniques, mainly during volatile
periods. Starting from the COVID-19 pandemic, the random forest model based on
both aggregate and disaggregated inflation achieves remarkable predictive
performance at intermediate and longer horizons.
