Machine learning methods for inflation forecasting in Brazil: New contenders versus classical models

In this paper, we explore machine learning (ML) methods to improve inflation forecasting in Brazil. An extensive out-of-sample forecasting exercise is designed with multiple horizons, a large database of 501 series, and 50 forecasting methods, including new ML techniques proposed here, traditional econometric models and forecast combination methods. We also provide tools to identify the key variables to predict inflation, thus helping to open the ML black box. Despite the evidence of no universal best model, the results indicate that ML methods can, in numerous cases, outperform traditional econometric models in terms of mean-squared error. Moreover, the results indicate the existence of nonlinearities in the inflation dynamics, which are relevant to forecasting inflation. The set of top forecasts often includes forecast combinations, tree-based methods (such as random forest and xgboost), breakeven inflation, and survey-based expectations. Altogether, these findings offer a valuable contribution to macroeconomic forecasting, especially, focused on Brazilian inflation.