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Quantile forecasts of inflation under model uncertainty

Dimitris Korobilis

No 2015-72, SIRE Discussion Papers from Scottish Institute for Research in Economics (SIRE)

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)
Date: 2015-04-30
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Downloads: (external link)
http://hdl.handle.net/10943/680

Related works:
Working Paper: Quantile forecasts of inflation under model uncertainty (2015) Downloads
Working Paper: Quantile forecasts of inflation under model uncertainty (2015) Downloads
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