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), pp. 11-20. (doi: 10.1016/j.ijforecast.2016.07.005)

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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:Articles
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
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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