The Factor-Lasso Approach for Inference in High-Dimensional Economic Applications | Hanlon Financial Systems Center

The Factor-Lasso Approach for Inference in High-Dimensional Economic Applications

The Factor-Lasso Approach for Inference in High-Dimensional Economic Applications

seminar date: 
Thursday, October 13, 2016 - 6:00pm
seminar location: 
Babbio 122
Dr. Yuan Liao, Associate Professor, Rutgers University
Abstract: 

We consider inference about coefficients on a small number of variables of interest in a linear panel data model with a large number of additional time-varying confounding variables. We allow the number of these additional confounding variables, p, to be larger than the sample size, and suppose that these confounding variables are generated by a small number of common factors and p weakly-dependent disturbances. We allow that both the factors and the disturbances are related to both the outcome variable and other variables of interest, and impose that the contribution of the part of the confounding variables not captured by time specific effects can be captured by a relatively small number of terms whose identities are unknown. Within this framework, we provide a simple computational algorithm based on factor extraction followed by lasso regression for inference about parameters of interest and show that the resulting procedure has good asymptotic properties. We also provide simulation evidence about performance of our procedure and illustrate its use in empirical applications. This is a joint work with Christian Hansen.

 

Bio: 

Dr. Yuan Liao works at the intersection of financial econometrics and statistics. Dr. Liao has developed econometric models for estimating the risk of portfolios of large dimensions. He also studied economic variable selection and forecast problems based on large economic datasets. 

Yuan’s research has appeared in numerous journals and books including Econometrica, Journal of Econometrics, Annals of Statistics, and The Oxford Handbook of Panel Data. He has given talks in conferences and seminars to the Research Section of the British Royal Statistical Association, Mathematisches Forschungsinstitut Obserwolfach, the Stevanovich Center at University of Chicago, Boston University, MIT, Georgetown, and many others. 

Dr. Liao is now an Associate Professor of Economics at Rutgers University. He received his Ph.D. in Statistics from Northwestern University in 2010. Before joining Rutgers, Dr. Liao held a position as assistant professor of statistics at University of Maryland (2012-2016), and worked at Princeton University as a postdoctoral associate (2010-2012).