Speaker：余俊(Jun Yu)

　　Title：Weak Identification of Long Memory with Implications for Inference

　　Schedule: March 8, Wed 1:00-2:30 PM

　　Location: 腾讯会议 893 945 674

　　Introduction：余俊是新加坡管理大学（Singapore Management University）李光前经济学和金融学教授，同时担任国际权威学术期刊计量经济学（Journal of Econometrics ）、计量经济学理论 （Econometric Theory ）和金融计量学（Journal of Financial Econometrics ）的副主编。目前, 余俊教授是国际金融计量学会 （Society for Financial Econometrics）的理事，也是第一位亚洲经济学者应邀担任该学会理事。他于2011年被Journal of Econometrics 推选为 Fellow of The Journal of Econometrics, 2012年被国际金融计量学会推选为Inaugural Fellow of Society for Financial Econometrics。余俊教授多年致力于把他的理论和方法运用于实践。他替多国中央银行、多家国际组织和大型公司做咨询，包括国际货币基金组织、香港金融管理局、新加坡金融管理局、新加坡政府投资公司、新加坡国家发展部、新西兰储备银行、新西兰财政部、东盟+3 宏观经济研究办公室、新加坡发地产发展商协会。

　　Abstract：This paper explores weak identification issues arising in commonly used models of economic and financial time series. Two highly popular configurations are shown to  be asymptotically observationally equivalent: one with long memory and weak autoregressive dynamics, the other with antipersistent shocks and a near-unit autoregressive root, often characterized as rough volatility in empirical work. This paper develops a data-driven semiparametric and identification-robust approach to inference that reveals these model ambiguities, investigates the implications of weak identification on forecasting, and documents the prevalence of weak identification in many realized volatility and trading volume series. Forecasting analyses at multiple horizons reveal advantages to long memory modeling; and the identification-robust empirical findings generally favor long memory dynamics in volatility and volume, a conclusion that is corroborated using social-media news flow data.