Horizon confidence sets

Jack Fosten () and Daniel Gutknecht ()
Additional contact information
Jack Fosten: King’s College London
Daniel Gutknecht: Goethe University Frankfurt

Empirical Economics, 2021, vol. 61, issue 2, No 5, 667-692

Abstract: Abstract This paper introduces a new statistical procedure to discriminate between competing forecasting models at different forecast horizons. Unlike existing tests, which eliminate a model from all horizons if dominated according to some loss measure, our methodology identifies an ‘optimal’ set of models at each horizon, retaining a model that performs well at a given horizon even if dominated at others. While our method is especially useful in applications to long-term forecasting as well as short-term nowcasting, it can also be applied in wider settings like the comparison of forecasting models across countries. We conduct a small Monte Carlo study to investigate the finite sample properties and apply our procedure to nowcasting US real GDP growth and its subcomponents, comparing a factor-based nowcasting method to a naïve benchmark. Unlike existing methods, ours can formally detect the point in the quarter at which the factor method beats the benchmark or vice versa.

Keywords: Nowcasting; Multiple model comparison; Model confidence set; Bootstrap (search for similar items in EconPapers)
JEL-codes: C12 C22 C52 C53 (search for similar items in EconPapers)
Date: 2021
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Citations: View citations in EconPapers (1)

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DOI: 10.1007/s00181-020-01891-7

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