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Smooth Robust Multi-Horizon Forecasts

Andrew B. Martinez (Office of Macroeconomic Analysis, U.S. Department of the Treasury, Washington DC, USA; H.O. Stekler Research Program on Forecasting, George Washington University, Washington DC, USA; Climate Econometrics, Nuffield College, Oxford, UK)
Jennifer L. Castle (Magdalen College, Climate Econometrics, and Institute for New Economic Thinking at the Oxford Martin School, University of Oxford, Oxford, UK)
David F. Hendry (Nuffield College, Climate Econometrics, and Institute for New Economic Thinking at the Oxford Martin School, University of Oxford, Oxford, UK)

Essays in Honor of M. Hashem Pesaran: Prediction and Macro Modeling

ISBN: 978-1-80262-062-7, eISBN: 978-1-80262-061-0

Publication date: 18 January 2022

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 UK productivity and US 10-year Treasury yields and show that they can dramatically reduce persistent forecast failure exhibited by forecasts from macroeconomic models and professional forecasters.

Keywords

Acknowledgements

Acknowledgments

The views expressed here are those of the authors and not necessarily those of the Treasury Department or the US Government. This research was supported in part by grants from the Robertson Foundation (grant 9907422) and from Nuffield College. Thanks to participants at the 22nd Dynamic Econometrics Conference and the 40th International Symposium on Forecasting, and to Allan Timmermann, Tommaso Proietti, and an anonymous referee for helpful comments and suggestions.

Citation

Martinez, A.B., Castle, J.L. and Hendry, D.F. (2022), "Smooth Robust Multi-Horizon Forecasts", Chudik, A., Hsiao, C. and Timmermann, A. (Ed.) Essays in Honor of M. Hashem Pesaran: Prediction and Macro Modeling (Advances in Econometrics, Vol. 43A), Emerald Publishing Limited, Leeds, pp. 143-165. https://doi.org/10.1108/S0731-90532021000043A008

Publisher

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Emerald Publishing Limited

Copyright © 2022 Andrew B. Martinez, Jennifer L. Castle and David F. Hendry

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