Quantile forecasts of inflation under model uncertainty

Dimitris Korobilis

Working Papers from Business School - Economics, University of Glasgow

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 (search for similar items in EconPapers)
JEL-codes: C11 C22 C52 (search for similar items in EconPapers)
Date: 2015-04
New Economics Papers: this item is included in nep-for and nep-ore
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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Related works:
Working Paper: Quantile forecasts of inflation under model uncertainty (2015)
Working Paper: Quantile forecasts of inflation under model uncertainty (2015)
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Persistent link: https://EconPapers.repec.org/RePEc:gla:glaewp:2015_09

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