On Testing for Diagonality of Large Dimensional Covariance Matrices
AbstractDatasets 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.
Download InfoIf you experience problems downloading a file, check if you have the proper application to view it first. In case of further problems read the IDEAS help page. Note that these files are not on the IDEAS site. Please be patient as the files may be large.
Bibliographic InfoPaper provided by Queen Mary, University of London, School of Economics and Finance in its series Working Papers with number 526.
Date of creation: Oct 2004
Date of revision:
Panel data; Large sample covariance matrix; Maximum eigenvalue;
Find related papers by JEL classification:
- C12 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Hypothesis Testing: General
- C15 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Statistical Simulation Methods: General
- C23 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Models with Panel Data; Spatio-temporal Models
This paper has been announced in the following NEP Reports:
Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.:
- Chang, Yoosoon, 2002.
"Nonlinear IV unit root tests in panels with cross-sectional dependency,"
Journal of Econometrics,
Elsevier, vol. 110(2), pages 261-292, October.
- Chang, Yoosoon, 2002. "Nonlinear IV Unit Root Tests in Panels with Cross-Sectional Dependency," Working Papers 2000-08, Rice University, Department of Economics.
- Yoosoon Chang, 2000. "Nonlinear IV Unit Root Tests in Panels with Cross-Sectional Dependency," CIRJE F-Series CIRJE-F-85, CIRJE, Faculty of Economics, University of Tokyo.
- Baltagi, Badi H. & Feng, Qu & Kao, Chihwa, 2012.
"A Lagrange Multiplier test for cross-sectional dependence in a fixed effects panel data model,"
Journal of Econometrics,
Elsevier, vol. 170(1), pages 164-177.
- Badi H. Baltagi & Qu Feng & Chihwa Kao, 2012. "A Lagrange Multiplier Test for Cross-Sectional Dependence in a Fixed Effects Panel Data Model," Center for Policy Research Working Papers 137, Center for Policy Research, Maxwell School, Syracuse University.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Nick Vriend).
If references are entirely missing, you can add them using this form.