论文标题：Improved Recession Dating Using Stock Market Volatility

　　发表时间：2020

　　论文所有作者：Yu-Fan Huang, Richard Startz

　　期刊名及所属分类： International Journal of Forecasting (国际B)

　　英文摘要：We offer an improved dating of U.S. business cycle turning points both retrospectively and in real time. This improvement is made possible by augmenting existing Markov-switching dynamic factor models with additional information on the stock return volatility. The model improves the prediction of the state of the economy using fully revised data significantly. Real-time identification can be made noticeably earlier than the NBER announcements, beating both the peak and trough announcements for recent recessions by several months.

　　中文摘要：我们提供了美国商业周期转折点的追溯和实时数据。这一改进是通过在现有的马尔可夫转换动态因子模型中增加股票收益波动的信息而得以实现的。该模型利用完全修正后的数据显著改善了对经济状况的预测。实时识别可以明显早于NBER的公告，比最近衰退的峰值和低谷公告都要早几个月。