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Predicting binary outcomes based on the pair-copula construction
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AI
DOI：10.1007/s00181-023-02418-6.png)
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Abstract
En 中文
We develop a new econometric model for the purpose of predicting binary outcomes based on an ensemble of predictors. The method uses the pair-copula construction (PCC) to optimally combine diverse information. As a building block of PCC, the conditional copula is permitted to depend on the conditioning variable in a nonparametric way. This is the major methodological departure from our previous work. We apply this methodology to predict US business cycle peaks 6 months ahead based on the three prominent leading indicators currently used by The Conference Board. In terms of the predictive accuracy as measured by the receiver operating characteristic curve, the proposed scheme is found to do well in comparison with some popular combination models. We have also evaluated the probability forecasts generated from these models using a battery of diagnostic tools, each of which reveals different aspects of skill of the generated forecasts.
Keywords:
Pair-copula construction
Local likelihood
Receiver operating characteristic curve
Calibration
Recession
Journal
E
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