Arbeitspapier
On testing for diagonality of large dimensional covariance matrices
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.
- Language
-
Englisch
- Bibliographic citation
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Series: Working Paper ; No. 526
- Classification
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Wirtschaft
Hypothesis Testing: General
Statistical Simulation Methods: General
Single Equation Models; Single Variables: Panel Data Models; Spatio-temporal Models
- Subject
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Panel data, Large sample covariance matrix, Maximum eigenvalue
Matrizenrechnung
Varianzanalyse
Korrelation
- Event
-
Geistige Schöpfung
- (who)
-
Kapetanios, George
- Event
-
Veröffentlichung
- (who)
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Queen Mary University of London, Department of Economics
- (where)
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London
- (when)
-
2004
- Handle
- Last update
-
10.03.2025, 11:46 AM CET
Data provider
This object is provided by:
ZBW - Deutsche Zentralbibliothek für Wirtschaftswissenschaften - Leibniz-Informationszentrum Wirtschaft. If you have any questions about the object, please contact the data provider.
ZBW - Deutsche Zentralbibliothek für Wirtschaftswissenschaften - Leibniz-Informationszentrum Wirtschaft. If you have any questions about the object, please contact the data provider.
Object type
- Arbeitspapier
Associated
- Kapetanios, George
- Queen Mary University of London, Department of Economics
Time of origin
- 2004
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