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This paper examines the effectiveness of several forecasting methods forpredicting inflation, focusing on aggregating disaggregated forecasts - alsoknown in the literature as the bottom-up approach. Taking the Brazilian case asan application, we consider different disaggregation levels for inflation andemploy a range of traditional time series techniques as well as linear andnonlinear machine learning (ML) models to deal with a larger number ofpredictors. For many forecast horizons, the aggregation of disaggregatedforecasts performs just as well survey-based expectations and models thatgenerate forecasts using the aggregate directly. Overall, ML methods outperformtraditional time series models in predictive accuracy, with outstandingperformance in forecasting disaggregates. Our results reinforce the benefits ofusing models in a data-rich environment for inflation forecasting, includingaggregating disaggregated forecasts from ML techniques, mainly during volatileperiods. Starting from the COVID-19 pandemic, the random forest model based onboth aggregate and disaggregated inflation achieves remarkable predictiveperformance at intermediate and longer horizons.

Forecasting inflation using disaggregates and machine learning