Wednesday, October 7, 2020 - 3:30pm to 4:30pm
- Zoom Meeting
Title: Large Dimensional Latent Factor Modeling with Missing Observations and Applications to Causal Inference
This paper develops the inferential theory for latent factor models estimated from large dimensional panel data with missing observations. We estimate a latent factor model by applying principal component analysis to an adjusted covariance matrix estimated from partially observed panel data. We derive the asymptotic distribution for the estimated factors, loadings and the imputed values under a general approximate factor model. The key application is to estimate counterfactual outcomes in causal inference from panel data. The unobserved control group is modeled as missing values, which are inferred from the latent factor model. The inferential theory for the imputed values allows us to test for individual treatment effects at any time. We apply our method to portfolio investment strategies and find that around 15% of their average returns are significantly reduced by the academic publication of these strategies.
Ruoxuan Xiong is a postdoc at the Stanford Graduate School of Business. She received her Ph.D. in Management Science & Engineering from Stanford University in 2020. She will be an assistant professor in the Department of Quantitative Theory & Methods at Emory University starting in fall 2021. Her research is focused on developing new methods for causal inference, experimental design, and statistical learning in large dimensional data sets, with applications mainly in financial risk and healthcare. She has received awards including Honorable Mention in 2019 INFORMS George Nicholson Student Paper Competition and Finalist in 2020 MSOM Student Paper Competition.
September 29, 2020 - 1:36pm