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Quantile regression forecasts of inflation under model uncertainty

Korobilis, D (2017) 'Quantile regression forecasts of inflation under model uncertainty.' International Journal of Forecasting, 33 (1). 11 - 20. ISSN 0169-2070

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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 and better calibrated compared to those from BMA in the traditional regression model. Additionally, QR-BMA methods compare favorably to popular nonlinear specifications for US inflation.

Item Type: Article
Uncontrolled Keywords: Bayesian model averaging; Quantile regression; Inflation forecasts; Fan charts
Subjects: H Social Sciences > HA Statistics
Divisions: Faculty of Social Sciences > Essex Business School > Essex Finance Centre
Depositing User: Dimitris Korobilis
Date Deposited: 23 Nov 2016 12:13
Last Modified: 07 Aug 2019 21:15

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