Quantile forecasts of ination under model uncertainty
نویسنده
چکیده
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 di¤erent predictors to a¤ect di¤erent quantiles of the dependent variable. I show that quantile regression BMA methods can help reduce uncertainty regarding outcomes of future ination by providing superior predictive densities compared to mean regression models with and without BMA. Keywords: Bayesian model averaging; quantile regression; ination forecasts; fan charts JEL Classi cation: C11, C22, C52
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