Quantile Forecasts of Inflation Under Model Uncertainty
9 Pages Posted: 25 May 2015
Date Written: May 25, 2015
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: Suggested Citation