Machine Learning Methods for Inflation Forecasting in Brazil: new contenders versus classical models

Gustavo Araujo and Wagner Gaglianone

No 561, Working Papers Series from Central Bank of Brazil, Research Department

Abstract: In this paper, we explore machine learning (ML) methods to improve inflation forecasting in Brazil. An extensive out-of-sample forecasting exercise is designed with multiple horizons, a large database of 501 series, and 50 forecasting methods, including new machine learning techniques proposed here, traditional econometric models and forecast combination methods. We also provide tools to identify the key variables to predict inflation, thus helping to open the ML black box. Despite the evidence of no universal best model, the results indicate machine learning methods can, in numerous cases, outperform traditional econometric models in terms of mean-squared error. Moreover, the results indicate the existence of nonlinearities in the inflation dynamics, which are relevant to forecast inflation. The set of top forecasts often includes forecast combinations, tree-based methods (such as random forest and xgboost), breakeven inflation, and survey-based expectations. Altogether, these findings offer a valuable contribution to macroeconomic forecasting, especially, focused on Brazilian inflation.

Date: 2022-07
New Economics Papers: this item is included in nep-big, nep-cmp, nep-for and nep-mon
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)

Downloads: (external link)
https://www.bcb.gov.br/content/publicacoes/WorkingPaperSeries/wps561.pdf (application/pdf)

Related works:
Journal Article: Machine learning methods for inflation forecasting in Brazil: New contenders versus classical models (2023)
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:bcb:wpaper:561

Access Statistics for this paper

More papers in Working Papers Series from Central Bank of Brazil, Research Department
Bibliographic data for series maintained by Rodrigo Barbone Gonzalez ().