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

Marcio Garcia, Marcelo Medeiros () and Gabriel F.R. Vasconcelos

International Journal of Forecasting, 2017, vol. 33, issue 3, 679-693

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.

Keywords: Real-time inflation forecasting; Emerging markets; Shrinkage; Factor models; LASSO; Regression trees; Random forests; Complete subset regression; Machine learning; Model confidence set; Forecast combination; Expert forecasts (search for similar items in EconPapers)
Date: 2017
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Citations: View citations in EconPapers (37)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:intfor:v:33:y:2017:i:3:p:679-693

DOI: 10.1016/j.ijforecast.2017.02.002

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