Smooth Robust Multi-Horizon Forecasts
Andrew Martinez (),
Jennifer Castle () and
David Hendry ()
No 2020-009, Working Papers from The George Washington University, Department of Economics, H. O. Stekler Research Program on Forecasting
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 (search for similar items in EconPapers)
JEL-codes: C51 C53 (search for similar items in EconPapers)
Pages: 28 pages
New Economics Papers: this item is included in nep-ecm, nep-ets and nep-for
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https://www2.gwu.edu/~forcpgm/2020-009.pdf First version, 2020 (application/pdf)
Chapter: Smooth Robust Multi-Horizon Forecasts (2022)
Working Paper: Smooth Robust Multi-Horizon Forecasts (2021)
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Persistent link: https://EconPapers.repec.org/RePEc:gwc:wpaper:2020-009
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