Quantile Forecasts of Inflation Under Model Uncertainty

9 Pages Posted: 25 May 2015

See all articles by Dimitris Korobilis

Dimitris Korobilis

University of Glasgow - Adam Smith Business School

Date Written: May 25, 2015

Abstract

Bayesian model averaging (BMA) methods are regularly used to deal with model uncertainty in regression models. This paper shows how to introduce Bayesian model averaging methods in quantile regressions, and allow for different predictors to affect different quantiles of the dependent variable. I show that quantile regression BMA methods can help reduce uncertainty regarding outcomes of future inflation by providing superior predictive densities compared to mean regression models with and without BMA.

Keywords: Bayesian model averaging, quantile regression, inflation forecasts, fan charts

JEL Classification: C11, C22, C52

Suggested Citation

Korobilis, Dimitris, Quantile Forecasts of Inflation Under Model Uncertainty (May 25, 2015). Available at SSRN: https://ssrn.com/abstract=2610253 or http://dx.doi.org/10.2139/ssrn.2610253

Dimitris Korobilis (Contact Author)

University of Glasgow - Adam Smith Business School ( email )

40 University Avenue
Gilbert Scott Building
Glasgow, Scotland G12 8QQ
United Kingdom

HOME PAGE: http://https://sites.google.com/site/dimitriskorobilis/

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