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

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 vol...

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Bibliographic Details
Published inInternational journal of forecasting Vol. 33; no. 3; pp. 679 - 693
Main Authors Garcia, Márcio G.P., Medeiros, Marcelo C., Vasconcelos, Gabriel F.R.
Format Journal Article
LanguageEnglish
Published Elsevier B.V 01.07.2017
Subjects
Complete subset regression
Emerging markets
Expert forecasts
Factor models
Forecast combination
LASSO
Machine learning
Model confidence set
Random forests
Real-time inflation forecasting
Regression trees
Shrinkage
Expert forecasts
Regression trees
Emerging markets
LASSO
Machine learning
Forecast combination
Real-time inflation forecasting
Shrinkage
Factor models
Complete subset regression
Random forests
Model confidence set
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Summary: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.
ISSN:0169-2070
1872-8200
DOI:10.1016/j.ijforecast.2017.02.002