Real-time inflation forecasting with high-dimensional models: The case of Brazil

Abstract

We show that high-dimensional econometric models, such as shrinkage and complete subset regression, perform very well in the real-time forecasting of inflation in data-rich environments. We use Brazilian inflation as an application. It is ideal as an example because it exhibits a high short-term volatility, and several agents devote extensive resources to forecasting its short-term behavior. Thus, precise forecasts made by specialists are available both as a benchmark and as an important candidate regressor for the forecasting models. Furthermore, we combine forecasts based on model confidence sets and show that model combination can achieve superior predictive performances.