Smooth Robust Multi-Horizon Forecasts
Andrew Martinez,
Jennifer Castle and
David Hendry
No 2021-W01, Economics Papers from Economics Group, Nuffield College, University of Oxford
Abstract:
We investigate whether smooth robust methods for forecasting can help mitigate pronounced and persistent failure across multiple forecast horizons. We demonstrate that naive predictors are interpretable as local estimators of the long-run relationship with the advantage of adapting quickly after a break, but at a cost of additional forecast error variance. Smoothing over naive estimates helps retain these advantages while reducing the costs, especially for longer forecast horizons. We derive the performance of these predictors after a location shift, and confirm the results using simulations. We apply smooth methods to forecasts of U.K. productivity and U.S. 10-year Treasury yields and show that they can dramatically reduce persistent forecast failure exhibited by forecasts from macroeconomic models and professional forecasters.
Keywords: Location Shifts; Long differencing; Productivity forecasts; Robust forecasts. JEL codes: C51, C53 (search for similar items in EconPapers)
Pages: 28 pages
Date: 2021-01-14
New Economics Papers: this item is included in nep-for
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)
Downloads: (external link)
https://www.nuffield.ox.ac.uk/economics/Papers/202 ... HSR%202020-12-21.pdf (application/pdf)
Related works:
Chapter: Smooth Robust Multi-Horizon Forecasts (2022) 
Working Paper: Smooth Robust Multi-Horizon Forecasts (2020) 
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:nuf:econwp:2101
Access Statistics for this paper
More papers in Economics Papers from Economics Group, Nuffield College, University of Oxford Contact information at EDIRC.
Bibliographic data for series maintained by Maxine Collett ().