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Selecting a Model for Forecasting

Magdalen College and Climate Econometrics, University of Oxford, High Street, Oxford OX1 4AU, UK
Nuffield College, Climate Econometrics and Institute for New Economic Thinking at the Oxford Martin School, University of Oxford, Nuffield College, New Road, Oxford OX1 1NF, UK
Author to whom correspondence should be addressed.
Academic Editor: Neil Ericsson
Econometrics 2021, 9(3), 26;
Received: 9 November 2018 / Revised: 16 June 2021 / Accepted: 17 June 2021 / Published: 25 June 2021
(This article belongs to the Special Issue Celebrated Econometricians: David Hendry)
We investigate forecasting in models that condition on variables for which future values are unknown. We consider the role of the significance level because it guides the binary decisions whether to include or exclude variables. The analysis is extended by allowing for a structural break, either in the first forecast period or just before. Theoretical results are derived for a three-variable static model, but generalized to include dynamics and many more variables in the simulation experiment. The results show that the trade-off for selecting variables in forecasting models in a stationary world, namely that variables should be retained if their noncentralities exceed unity, still applies in settings with structural breaks. This provides support for model selection at looser than conventional settings, albeit with many additional features explaining the forecast performance, and with the caveat that retaining irrelevant variables that are subject to location shifts can worsen forecast performance. View Full-Text
Keywords: model selection; forecasting; location shifts; significance level; Autometrics model selection; forecasting; location shifts; significance level; Autometrics
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MDPI and ACS Style

Castle, J.L.; Doornik, J.A.; Hendry, D.F. Selecting a Model for Forecasting. Econometrics 2021, 9, 26.

AMA Style

Castle JL, Doornik JA, Hendry DF. Selecting a Model for Forecasting. Econometrics. 2021; 9(3):26.

Chicago/Turabian Style

Castle, Jennifer L., Jurgen A. Doornik, and David F. Hendry. 2021. "Selecting a Model for Forecasting" Econometrics 9, no. 3: 26.

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