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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

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 (search for similar items in EconPapers)
JEL-codes: C51 C53 (search for similar items in EconPapers)
Pages: 28 pages
Date: 2020-12
New Economics Papers: this item is included in nep-ecm, nep-ets and nep-for
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Citations: View citations in EconPapers (2) Track citations by RSS feed

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https://www2.gwu.edu/~forcpgm/2020-009.pdf First version, 2020 (application/pdf)

Related works:
Chapter: Smooth Robust Multi-Horizon Forecasts (2022) Downloads
Working Paper: Smooth Robust Multi-Horizon Forecasts (2021) Downloads
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Persistent link: https://EconPapers.repec.org/RePEc:gwc:wpaper:2020-009

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