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Article

Forecasting Principles from Experience with Forecasting Competitions

1
Magdalen College and Institute for New Economic Thinking at the Oxford Martin School, University of Oxford, High Street, Oxford OX1 4AU, UK
2
Institute for New Economic Thinking at the Oxford Martin School, and Climate Econometrics, Nuffield College, University of Oxford, New Road, Oxford OX1 1NF, UK
*
Author to whom correspondence should be addressed.
Received: 25 January 2021 / Revised: 11 February 2021 / Accepted: 12 February 2021 / Published: 23 February 2021
(This article belongs to the Special Issue New Frontiers in Forecasting the Business Cycle and Financial Markets)
Economic forecasting is difficult, largely because of the many sources of nonstationarity influencing observational time series. Forecasting competitions aim to improve the practice of economic forecasting by providing very large data sets on which the efficacy of forecasting methods can be evaluated. We consider the general principles that seem to be the foundation for successful forecasting, and show how these are relevant for methods that did well in the M4 competition. We establish some general properties of the M4 data set, which we use to improve the basic benchmark methods, as well as the Card method that we created for our submission to that competition. A data generation process is proposed that captures the salient features of the annual data in M4. View Full-Text
Keywords: automatic forecasting; calibration; prediction intervals; regression; forecasting competitions; seasonality; software; time series; nonstationarity automatic forecasting; calibration; prediction intervals; regression; forecasting competitions; seasonality; software; time series; nonstationarity
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MDPI and ACS Style

Castle, J.L.; Doornik, J.A.; Hendry, D.F. Forecasting Principles from Experience with Forecasting Competitions. Forecasting 2021, 3, 138-165. https://0-doi-org.brum.beds.ac.uk/10.3390/forecast3010010

AMA Style

Castle JL, Doornik JA, Hendry DF. Forecasting Principles from Experience with Forecasting Competitions. Forecasting. 2021; 3(1):138-165. https://0-doi-org.brum.beds.ac.uk/10.3390/forecast3010010

Chicago/Turabian Style

Castle, Jennifer L., Jurgen A. Doornik, and David F. Hendry 2021. "Forecasting Principles from Experience with Forecasting Competitions" Forecasting 3, no. 1: 138-165. https://0-doi-org.brum.beds.ac.uk/10.3390/forecast3010010

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