Title: Theory and Application of Large Covariance Matrix Estimation in Panel Data Models
- Speaker: Dr. Yuan Liao, Department of Mathematics, University of Maryland
- Date & Time: Friday, February 28, 11-12 pm
- Location: Phillips Hall, Room 110 (801 22nd Street, NW, Washington, DC 20052)
- Directions: Foggy Bottom-GWU Metro Stop on the Orange and Blue Lines. The campus map is at http://www.gwu.edu/explore/visitingcampus/campusmaps.
- Sponsor: The George Washington University, Department of Statistics. See http://departments.columbian.gwu.edu/statistics/academics/seminars for a list of seminars.
Abstract:
High dimensional covariance matrix estimation has seen its wide applications in panel data models and factor analysis. While the sparsity assumption on the covariance matrix directly might be restrictive, it is more reasonable to be satisfied when common factors are controlled first. This so-called "conditional sparsity (given factors)" assumption enables us to estimate various covariance matrices with good rate of convergence. Some applications in portfolio allocation are also presented.
