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Forecasting Brazilian Inflation with High-Dimensional Models

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  • Medeiros, Marcelo C
  • Vasconcelos, Gabriel
  • Freitas, Eduardo

Abstract

In this paper we use high-dimensional models, estimated by the Least Absolute Shrinkage and Selection Operator (LASSO), to forecast the Brazilian inflation. The models are compared to benchmark specifications such as linear autoregressive (AR) and the factor models based on principal components. Our results showed that the LASSO-based specifications have the smallest errors for short-horizon forecasts. However, for long horizons the AR benchmark is the best model with respect to point forecasts. The factor model also produces some good long horizon forecasts in a few cases. We estimated all the models for the two most important Brazilian inflation measures, the IPCA and the IGP-M indexes. The results also showed that there are differences on the selected variables for both measures. Finally, the most important variables selected by the LASSO based models are, in general, related to government debt and money. On the other hand, variables such as unemployment and production were rarely selected by the LASSO.

Suggested Citation

  • Medeiros, Marcelo C & Vasconcelos, Gabriel & Freitas, Eduardo, 2016. "Forecasting Brazilian Inflation with High-Dimensional Models," Brazilian Review of Econometrics, Sociedade Brasileira de Econometria - SBE, vol. 36(2), November.
    • Handle: RePEc:sbe:breart:v:36:y:2016:i:2:a:52273
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    5. Garcia, Márcio G.P. & Medeiros, Marcelo C. & Vasconcelos, Gabriel F.R., 2017. "Real-time inflation forecasting with high-dimensional models: The case of Brazil," International Journal of Forecasting, Elsevier, vol. 33(3), pages 679-693.
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    7. Carlos Medel, 2021. "Forecasting Brazilian Inflation with the Hybrid New Keynesian Phillips Curve: Assessing the Predictive Role of Trading Partners," Working Papers Central Bank of Chile 900, Central Bank of Chile.

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