A Comparison of the Real-Time Performance of Business Cycle Dating Methods
评估了两种正式规则（非参数算法和马尔可夫转换动态因子模型）在实时确定美国商业周期转折点日期的能力，发现两者在过去30年都能准确识别NBER周期，且马尔可夫模型在识别谷底速度上优于NBER。
AbstractWe evaluate the ability of formal rules to establish U.S. business cycle turning point dates in real time. We consider two approaches, a nonparametric algorithm and a parametric Markov-switching dynamic-factor model. Using a new “real-time” dataset of coincident monthly variables, we find that both approaches would have accurately identified the NBER business cycle chronology had they been in use over the past 30 years, with the Markov-switching model most closely matching the NBER dates. Further, both approaches, and particularly the Markov-switching model, yielded significant improvement over the NBER in the speed with which business cycle troughs were identified.KEY WORDS: Dynamic-factor modelMarkov-switchingRecessionTurning pointVintage data