Dingxian Cao is a highly trained Statistician and Econometrician with 5+ years of research experience, cooperating with interdisciplinary teams, developing most frontier machine learning/big data models to identify true information and value. And delivering interpretation of techniques to the audience.
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PhD in Economics, 2022
University of Connecticut
MS in Statistics, 2017
University of Wisconsin - Madison
BS in Statistics, 2016
East China Normal University
This paper proposes a uniform inference method for the autoregressive coefficient of a dynamic panel model. Our method overcomes the asymptotic discontinuity of bias-corrected maximum livelihood (ML) between stationary and unit root parameter space. The proposed self-normalized t-statistic can be used to construct a confidence set that provides a uniformly valid asymptotic coverage over the whole parameter space.