Date of Award
5-2013
Document Type
Thesis
Degree Name
Master of Science (MS)
Legacy Department
Mathematical Science
Committee Chair/Advisor
Luo, June
Committee Member
Gallagher , Colin
Committee Member
Gerard , Patrick
Abstract
Approximate factor models are popular in finance and economics. A key to effectively utilizing such a model is to accurately estimate the error covariance matrix. Errors related to certain predictors are expected to be correlated and this must be modeled effectively. Adaptive thresholding is a method for estimating the error covariance matrix of such a model. This method is described in detail and a simulation study sheds light on the behavior of this method under different sample sizes and parameterizations.
Recommended Citation
Chimenti, Paul, "Error Covariance Matrix Estimation in High Dimensional Approximate Factor Models Using Adaptive Thresholding: A Simulation Study" (2013). All Theses. 1577.
https://tigerprints.clemson.edu/all_theses/1577