On rank estimation in symmetric matrices: the case of indefinite matrix estimators

Stephen Donald, Natércia Fortuna and Vladas Pipiras ()
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Vladas Pipiras: University of North Carolina at Chapel Hill

FEP Working Papers from Universidade do Porto, Faculdade de Economia do Porto

Abstract: We focus on the problem of rank estimation in an unknown symmetric matrix based on a symmetric, asymptotically normal estimator of the matrix. The related positive definite limit covariance matrix is assumed to be estimated consistently, and to have either a Kronecker product or an arbitrary structure. These assumptions are standard although they also exclude the case when the matrix estimator is positive or negative semidefinite. We adapt and reexamine here some available rank tests, and introduce a new rank test based on the eigenvalues of the matrix estimator. We discuss several applications where rank estimation in symmetric matrices is of interest, and also provide a small simulation study and an application.

Keywords: rank; symmetric matrix; eigenvalues; matrix decompositions; estimation; asymptotic normality; consistency (search for similar items in EconPapers)
JEL-codes: C12 C13 (search for similar items in EconPapers)
Pages: 27 pages.
Date: 2005-02
New Economics Papers: this item is included in nep-ecm and nep-ets
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (4)

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Related works:
Journal Article: ON RANK ESTIMATION IN SYMMETRIC MATRICES: THE CASE OF INDEFINITE MATRIX ESTIMATORS (2007)
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Persistent link: https://EconPapers.repec.org/RePEc:por:fepwps:167

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