Abstract
This paper constructs a composite leading index for business cycle prediction based on vine copulas that capture the complex pattern of dependence among individual predictors. This approach is optimal in the sense that the resulting index possesses the highest discriminatory power as measured by the receiver operating characteristic (ROC) curve. The model specification is semi-parametric in nature, suggesting a two-step estimation procedure, with the second-step finite dimensional parameter being estimated by QMLE given the first-step non-parametric estimate. To illustrate its usefulness, we apply this methodology to optimally aggregate the 10 leading indicators selected by The Conference Board (TCB) to predict economic recessions in the United States. In terms of the discriminatory power, our method is significantly better than the Index used by TCB.
Funding source: National Science Foundation of China
Award Identifier / Grant number: 71603115
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Author contribution: All the authors have accepted responsibility for the entire content of this submitted manuscript and approved submission.
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Research funding: This research was supported by the National Science Foundation of China (grant number 71603115).
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Conflict of interest statement: The authors declare no conflicts of interest regarding this article.
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Supplementary Material
The online version of this article offers supplementary material https://doi.org/10.1515/snde-2019-0033.
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