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Description:
I propose a new model, conditional quantile regression (CQR), that generates density forecasts consistent with a specific view of the future evolution of some variables. This addresses a shortcoming of existing quantile regression-based models, for example the at-risk framework popularised by Adrian et al. (2019), when used in settings, such as most forecasting processes within central banks and similar institutions, that require forecasts to be conditional on a set of technical assumptions. Through an application to house price inflation in the euro area, I show that CQR provides a viable alternative to existing approaches to conditional density forecasting, notably Bayesian VARs, with considerable advantages in terms of flexibility and additional insights that do not come at the cost of forecasting performance.