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.

翻译：本文考察了多种预测通胀方法的有效性，重点关注分解预测的汇总——文献中亦称为自下而上方法。以巴西案例为应用场景，我们考虑了不同层次的通胀分解，并采用多种传统时间序列技术以及线性和非线性机器学习模型来处理大量预测变量。在多个预测期上，分解预测的汇总表现与基于调查的预期及直接使用总体数据生成预测的模型不相上下。总体而言，机器学习方法在预测准确性上优于传统时间序列模型，尤其在分解预测中表现突出。我们的结果强化了在数据丰富环境中使用模型进行通胀预测的益处，包括将机器学习技术生成的分解预测进行汇总，特别是在波动时期。自新冠疫情期间起，基于总体与分解通胀数据的随机森林模型在中长期预测期上展现出卓越的预测性能。