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

Márcio G.P. Garcia, Marcelo C. Medeiros, Gabriel F.R. Vasconcelos

Research output: Contribution to journalArticlepeer-review


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.

Original languageEnglish (US)
Pages (from-to)679-693
Number of pages15
JournalInternational Journal of Forecasting
Issue number3
StatePublished - Jul 1 2017
Externally publishedYes


  • Complete subset regression
  • Emerging markets
  • Expert forecasts
  • Factor models
  • Forecast combination
  • Machine learning
  • Model confidence set
  • Random forests
  • Real-time inflation forecasting
  • Regression trees
  • Shrinkage

ASJC Scopus subject areas

  • Business and International Management


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