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【11月8日】【中央财经大学经济学院“龙马经济学双周学术论坛”】2017年秋季学期第三讲·Robust detection of spurious regressions involving processes moderately deviated from a unit root

[发表时间]:2017-11-06 [来源]:经济学院 [浏览次数]:

    主讲嘉宾:涂云东研究员    北京大学
    讲座时间:2017年11月8日(周三)  15:00--17:00
    讲座地点:学术会堂606

    嘉宾简介:
    涂云东,北京大学光华管理学院商务统计与经济计量系和北京大学统计科学中心联席助理教授,研究员。2004年获武汉大学数学与统计学院信息与计算科学专业学士学位,2006年获武汉大学经济与管理学院数量经济学专业硕士学位,2012年获美国加州大学河滨分校经济学博士学位,同年6月加入北大光华。曾获世界计量经济学会(Econometric Society),加州计量经济学会议等学术组织提供的青年学者研究资助以及Phi Beta Kappa International Scholarship Award。学术论文发表在Journal of Econometrics, Econometric Reviews, Journal of Business and Economic Statistics,Statistica Sinica等国际一流专业杂志。同时担任以下学术期刊匿名评审:Annals of Statistics, Econometric Reviews, Empirical Economics, Journal of Business and Economic Statistics, Journal of Econometrics, Studies in Nonlinear Dynamics and Econometrics, Journal of Quantitative Economics。理论研究领域涵盖非参数/半参数计量经济模型,模型选择和模型平均,网络数据建模,金融计量,信息计量经济学,模型设定检验等;应用研究包含宏观经济预测,价格指数建模,网络数据分析,股票市场预测,生产率建模等。

    内容摘要:
    This paper studies spurious regressions involving processes moderately deviated from a unit root, and establishes the limiting distributions for the least squares estimator, the associated $t$-statistic, the coefficient of determination $R^2$ and the Durbin-Watson statistic. We find that these limiting distributions depend on nuisance parameters, which makes spurious effects detection impossible using the conventional $t$-statistic in practice. As a cure, we propose a robust $t$-test based on the balanced regression model, where the lagged regressor and the lagged dependent variable are augmented to the original regression. The induced $t$-statistic via such an augmentation is shown to be asymptotically standard normal and free of nuisance parameters. Furthermore, the limiting properties of other statistics are show to be similar to those in the classical regression theory. Such spurious detective method is very easy to implement in practice. Finally, the finite sample properties of the robust detective method are demonstrated through Monte Carlo studies and empirical examples.


[编辑]:张萌