Please use this identifier to cite or link to this item: http://hdl.handle.net/10419/107305
Authors: 
Lahiri, Kajal
Yang, Liu
Year of Publication: 
2015
Series/Report no.: 
CESifo Working Paper 5175
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.
Subjects: 
receiver operating characteristic curve
Copula
Bayesian methods
Markov chain Monte Carlo
yield spread
ISM diffusion index
JEL: 
C11
C15
C38
C53
E37
Document Type: 
Working Paper

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