Forecasting inflation using disaggregates and machine learning

TL;DR

This paper evaluates various forecasting methods for inflation in Brazil, highlighting that machine learning models, especially random forests, excel in predictive accuracy, particularly during volatile periods like the COVID-19 pandemic.

Contribution

It demonstrates the effectiveness of aggregating disaggregated forecasts using machine learning models, notably random forests, for improved inflation prediction.

Findings

ML models outperform traditional time series models in accuracy.

Aggregating disaggregated forecasts is as effective as direct models.

Random forest models show remarkable performance during volatile periods.

Abstract

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.