Semiparametric model for covariance regression analysis

Jin Liu, Yingying Ma and Hansheng Wang

Computational Statistics & Data Analysis, 2020, vol. 142, issue C

Abstract: Estimating covariance matrices is an important research topic in statistics and finance. A semiparametric model for covariance matrix estimation is proposed. Specifically, the covariance matrix is modeled as a polynomial function of the symmetric adjacency matrix with time varying parameters. The asymptotic properties for the time varying coefficient and the associated semiparametric covariance estimators are established. A Bayesian information criterion to select the order of the polynomial function is also investigated. Simulation studies and an empirical example are presented to illustrate the usefulness of the proposed method.

Keywords: Adjacency matrix; Covariance estimation; Covariance regression; Information criterion; Time varying coefficient (search for similar items in EconPapers)
Date: 2020
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Citations: View citations in EconPapers (1)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:csdana:v:142:y:2020:i:c:s0167947319301628

DOI: 10.1016/j.csda.2019.106815

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