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Semiparametric model for covariance regression analysis

Author

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  • Liu, Jin
  • Ma, Yingying
  • Wang, Hansheng

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.

Suggested Citation

  • Liu, Jin & Ma, Yingying & Wang, Hansheng, 2020. "Semiparametric model for covariance regression analysis," Computational Statistics & Data Analysis, Elsevier, vol. 142(C).
    • Handle: RePEc:eee:csdana:v:142:y:2020:i:c:s0167947319301628
    DOI: 10.1016/j.csda.2019.106815
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    Cited by:

    1. Guanyu Hu & Yishu Xue & Zhihua Ma, 2020. "Bayesian Clustered Coefficients Regression with Auxiliary Covariates Assistant Random Effects," Papers 2004.12022, arXiv.org, revised Aug 2021.

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