Fan charts 2.0: Flexible forecast distributions with expert judgement

Abstract

I propose a new model, conditional quantile regression (CQR), that generates density forecasts consistent with a specific view of the future evolution of some of the explanatory variables. This addresses a shortcoming of existing quantile regression-based models in settings that require forecasts to be conditional on technical assumptions, such as most forecasting processes within policy institutions. Through an application to house price inflation in the euro area, I show that CQR provides a viable alternative to conditional density forecasting with Bayesian VARs, with added flexibility and further insights that do not come at the cost of forecasting performance.