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Quantile regression forecasts of inflation under model uncertainty

Dimitris Korobilis

International Journal of Forecasting, 2017, vol. 33, issue 1, 11-20

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.

Keywords: Bayesian model averaging; Quantile regression; Inflation forecasts; Fan charts (search for similar items in EconPapers)
Date: 2017
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Handle: RePEc:eee:intfor:v:33:y:2017:i:1:p:11-20