Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/62858 
Year of Publication: 
2004
Series/Report no.: 
Working Paper No. 526
Publisher: 
Queen Mary University of London, Department of Economics, London
Abstract: 
Datasets in a variety of disciplines require methods where both the sample size and the dataset dimensionality are allowed to be large. This framework is drastically different from the classical asymptotic framework where the number of observations is allowed to be large but the dimensionality of the dataset remains fixed. This paper proposes a new test of diagonality for large dimensional covariance matrices. The test is based on the work of John (1971) and Ledoit and Wolf (2002) among others. The theoretical properties of the test are discussed. A Monte Carlo study of the small sample properties of the test indicate that it behaves well under the null hypothesis and has superior power properties compared to an existing test of diagonality for large datasets.
Subjects: 
Panel data, Large sample covariance matrix, Maximum eigenvalue
JEL: 
C12
C15
C23
Document Type: 
Working Paper

Files in This Item:
File
Size
206.76 kB





Items in EconStor are protected by copyright, with all rights reserved, unless otherwise indicated.