Forecasting Recessions with Financial Variables and Temporal Dependence

Extending earlier research on forecasting recessions with financial variables, I examine the importance of additional financial variables and temporal dependence for recession prediction. I show that both additional financial variables, in particular, the Treasury bill spread, default yield spread, stock return volatility, and temporal cubic terms, which account for temporal dependence, independently help to improve not only in-sample, but also out-of-sample recession prediction. I also find that additional financial variables and temporal cubic terms complement each other in enhancing the predictability of recessions, increasing the explanatory power and decreasing prediction error further, compared to their individual performance.

Construction of leading economic index for recession prediction using vine copulas

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.