Quantile regression forecasts of inflation under model uncertainty

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• 作者：Dimitris Korobilis ^{Dimitris.Korobilis@glasgow.ac.uk}Author Vitae
• 关键词：Bayesian model averaging ; Quantile regression ; Inflation forecasts ; Fan charts
• 刊名：International Journal of Forecasting
• 出版年：2017
• 出版时间：January-March 2017
• 年：2017
• 卷：33
• 期：1
• 页码：11-20
• 全文大小：504 K
• 卷排序：33

文摘

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.