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Quantile regression forecasts of inflation under model uncertainty

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  • Korobilis, Dimitris

Abstract

This paper examines the performance of Bayesian model averaging (BMA) methods in a quantile regression model for inflation. Different predictors are allowed to affect different quantiles of the dependent variable. Based on real-time quarterly data for the US, we show that quantile regression BMA (QR-BMA) predictive densities are superior to and better calibrated than those from BMA in the traditional regression model. In addition, QR-BMA methods also compare favorably to popular nonlinear specifications for US inflation.

Suggested Citation

  • Korobilis, Dimitris, 2017. "Quantile regression forecasts of inflation under model uncertainty," International Journal of Forecasting, Elsevier, vol. 33(1), pages 11-20.
    • Handle: RePEc:eee:intfor:v:33:y:2017:i:1:p:11-20
    DOI: 10.1016/j.ijforecast.2016.07.005
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