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A non-linear forecast combination procedure for binary outcomes

A non-linear forecast combination procedure for binary outcomes AbstractWe 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. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Studies in Nonlinear Dynamics & Econometrics de Gruyter

A non-linear forecast combination procedure for binary outcomes

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Publisher
de Gruyter
Copyright
©2016 Walter de Gruyter GmbH, Berlin/Boston
ISSN
1558-3708
eISSN
1558-3708
DOI
10.1515/snde-2014-0054
Publisher site
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Abstract

AbstractWe 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.

Journal

Studies in Nonlinear Dynamics & Econometrics – de Gruyter

Published: Sep 1, 2016

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