City Research Online

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.

Publication Type:	Article
Additional Information:	This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.
Publisher Keywords:	Nowcasting, Multiple model comparison, Model confidence set, Bootstrap
Subjects:	H Social Sciences > HA Statistics H Social Sciences > HB Economic Theory
Departments:	Bayes Business School Bayes Business School > Finance
SWORD Depositor:	Symplectic Administrator

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