Reduced-Rank Regression: A Useful Determinant Identity
We derive an identity for the determinant of a product involving non-squared matrices. The identity can be used to derive the maximum likelihood estimator in reduced-rank regressions with Gaussian innovations. Furthermore, the identity sheds light on the structure of the estimation problem that arises when the reduced-rank parameters are subject to additional constraints.
|Date of creation:||15 Jan 2008|
|Date of revision:|
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- Peter Reinhard Hansen, 2000.
"Structural Changes in the Cointegrated Vector Autoregressive Model,"
2000-20, Brown University, Department of Economics.
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