A New Parametrization of Correlation Matrices

Ilya Archakov and Peter Hansen

Papers from arXiv.org

Abstract: We introduce a novel parametrization of the correlation matrix. The reparametrization facilitates modeling of correlation and covariance matrices by an unrestricted vector, where positive definiteness is an innate property. This parametrization can be viewed as a generalization of Fisther's Z-transformation to higher dimensions and has a wide range of potential applications. An algorithm for reconstructing the unique n x n correlation matrix from any d-dimensional vector (with d = n(n-1)/2) is provided, and we derive its numerical complexity.

Date: 2020-12
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Citations: View citations in EconPapers (3)

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Journal Article: A New Parametrization of Correlation Matrices (2021)
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