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dc.contributor.authorHafner, C.
dc.contributor.authorLinton, O.
dc.contributor.authorTang, H.
dc.date.accessioned2019-02-04T12:13:58Z
dc.date.available2019-02-04T12:13:58Z
dc.date.issued2018-09-28
dc.identifier.otherCWPE1878
dc.identifier.urihttps://www.repository.cam.ac.uk/handle/1810/288756
dc.description.abstractWe propose a Kronecker product model for correlation or covariance matrices in the large dimension case. The number of parameters of the model increases logarithmically with the dimension of the matrix. We propose a minimum distance (MD) estimator based on a log-linear property of the model, as well as a one-step estimator, which is a one-step approximation to the quasi-maximum likelihood estimator (QMLE).We establish the rate of convergence and a central limit theorem (CLT) for our estimators in the large dimensional case. A specification test and tools for Kronecker product model selection and inference are provided. In an Monte Carlo study where a Kronecker product model is correctly specified, our estimators exhibit superior performance. In an empirical application to portfolio choice for S&P500 daily returns, we demonstrate that certain Kronecker product models are good approximations to the general covariance matrix.
dc.publisherFaculty of Economics
dc.relation.ispartofseriesCambridge Working Papers in Economics
dc.rightsAll Rights Reserveden
dc.rights.urihttps://www.rioxx.net/licenses/all-rights-reserved/en
dc.subjectCorrelation matrix
dc.subjectKronecker product
dc.subjectMatrix logarithm
dc.subjectMultiway
dc.subjectarray data
dc.subjectPortfolio choice
dc.subjectSparsity
dc.titleEstimation of a Multiplicative Correlation Structure in the Large Dimensional Case
dc.typeWorking Paper
dc.identifier.doi10.17863/CAM.36017


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