Real-Time Inflation Forecasting with High-Dimensional Models: The Case of Brazil

International Journal of Forecasting V 33, N 3, P 679–693, 2017

Gabriel Vasconcelos, Marcelo Medeiros, Márcio Gomes Pinto Garcia.

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.

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