Korobilis, D. (2017) Quantile regression forecasts of inflation under model uncertainty. International Journal of Forecasting, 33(1), pp. 11-20. (doi: 10.1016/j.ijforecast.2016.07.005)
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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 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: | Articles |
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Status: | Published |
Refereed: | Yes |
Glasgow Author(s) Enlighten ID: | Korompilis Magkas, Professor Dimitris |
Authors: | Korobilis, D. |
College/School: | College of Social Sciences > Adam Smith Business School > Economics |
Journal Name: | International Journal of Forecasting |
Publisher: | Elsevier |
ISSN: | 0169-2070 |
Published Online: | 28 September 2016 |
Copyright Holders: | Copyright © 2016 International Institute of Forecasters |
First Published: | First published in International Journal of Forecasting 33(1): 11-20 |
Publisher Policy: | Reproduced in accordance with the publisher copyright policy |
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