Fan charts 2.0: flexible forecast distributions with expert judgement
Andrej Sokol
No 2624, Working Paper Series from European Central Bank
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 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. JEL Classification: C22, C53, E37, R31
Keywords: at-risk; conditional forecasting; density forecast evaluation; house prices; quantile regression (search for similar items in EconPapers)
Date: 2021-12
New Economics Papers: this item is included in nep-ecm, nep-eec, nep-for, nep-mac and nep-mon
References: View references in EconPapers View complete reference list from CitEc
Citations:
Downloads: (external link)
https://www.ecb.europa.eu//pub/pdf/scpwps/ecb.wp2624~4e679bae9b.en.pdf (application/pdf)
Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.
Export reference: BibTeX
RIS (EndNote, ProCite, RefMan)
HTML/Text
Persistent link: https://EconPapers.repec.org/RePEc:ecb:ecbwps:20212624
Access Statistics for this paper
More papers in Working Paper Series from European Central Bank 60640 Frankfurt am Main, Germany. Contact information at EDIRC.
Bibliographic data for series maintained by Official Publications ().