A Non-linear Forecast Combination Procedure for Binary Outcomes

Kajal Lahiri and Liu Yang

No 5175, CESifo Working Paper Series from CESifo

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.

Keywords: receiver operating characteristic curve; Copula; Bayesian methods; Markov chain Monte Carlo; yield spread; ISM diffusion index (search for similar items in EconPapers)
JEL-codes: C11 C15 C38 C53 E37 (search for similar items in EconPapers)
Date: 2015
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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Journal Article: A non-linear forecast combination procedure for binary outcomes (2016)
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