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A non-linear forecast combination procedure for binary outcomes

  • Kajal Lahiri and Liu Yang EMAIL logo

Abstract

We develop a non-linear forecast combination rule based on copulas that incorporate the dynamic interaction between individual predictors. This approach is optimal in the sense that the resulting combined forecast produces the highest discriminatory power as measured by the receiver operating characteristic (ROC) curve. Under additional assumptions, this rule is shown to be equivalent to the quintessential linear combination scheme. To illustrate its usefulness, we apply this methodology to optimally aggregate two currently used leading indicators – the ISM new order diffusion index and the yield curve spread – to predict economic recessions in the United States. We also examine the sources of forecasting gains using a counterfactual experimental set up.


Corresponding author: Liu Yang, Nanjing University – School of Economics, Nanjing, Jiangsu, China, e-mail:

Acknowledgments

The authors are grateful to James Ramsey and other participants at the 22nd SNDE Symposium in New York city, to Joerg Breitung, Frank Diebold, Hans Manner, Ataman Ozyildirim, Philip Rothman, Pravin Trivedi, David Zimmer, and an anonymous referee for making valuable comments and suggestions. However, we solely are responsible for any errors and omissions.

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Supplemental Material

The online version of this article (DOI: 10.1515/snde-2014-0054) offers supplementary material, available to authorized users.


Published Online: 2015-2-6
Published in Print: 2016-9-1

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