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Understanding Regressions with Observations Collected at High Frequency over Long Span
（长期高频观测数据下的回归分析）
主讲人：Ye Lu（School of Economics, University of Sydney）
主持老师：（北大经院）王熙
参与老师：（北大经院）王一鸣、刘蕴霆、王法
（北大国发院）黄卓、张俊妮、孙振庭
（北大新结构）胡博
时间：2023年6月9日（周五） 10:00-11:30
地点：8797威尼斯老品牌107会议室
报告摘要：
In this paper, we analyze regressions with observations collected at small time intervals over a long period of time. For the formal asymptotic analysis, we assume that samples are obtained from continuous time stochastic processes, and let the sampling interval δ shrink down to zero and the sample span T increase up to infinity. In this setup, we show that the standard Wald statistic diverges to infinity and the regression becomes spurious as long as δ → 0 sufficiently fast relative to T → ∞. Such a phenomenon is indeed what is frequently observed in practice for the type of regressions considered in the paper. In contrast, our asymptotic theory predicts that the spuriousness disappears if we use the robust version of the Wald test with an appropriate long-run variance estimate. This is supported, strongly and unambiguously, by our empirical illustration.
主讲人简介：
Ye Lu is currently a senior lecturer at the School of Economics, University of Sydney. Her work focuses on econometric theory, macroeconometrics, financial econometrics, and point process models. She has had several publications in the Journal of Econometrics.
