Measuring and Predicting Turning Points Using a Dynamic Bi-Factor Model

In this paper a dynamic bi-factor model with Markov-switching is developed to measure and predict turning points. Both common factors have their own cyclical dynamics and their lead-lag relationships are reflected in the transition probabilities matrix. The model is applied to four coincident and four selected leading indicators for the US economy. The bi-factor model stimates that, on average, CLI leads CCI by 7-8 months at both peaks and troughs. The model-derived recession probabilities of CCI and those of CLI with a lag of 9 months capture the NBER business cycle chronology very well. The out-of-sample forecast using CLI successfully detected the latest recession from March to December 2001. This ensures the measurement and prediction of turning points in a precise and timely fashion