By the same authors

From the same journal

Nonparametric Estimation of Large Covariance Matrices with Conditional Sparsity

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Published copy (DOI)

Author(s)

  • Hanchao Wang
  • Bin Peng
  • Degui Li
  • Chenlei Leng

Department/unit(s)

Publication details

JournalJournal of Econometrics
DateAccepted/In press - 28 Sep 2020
DateE-pub ahead of print - 23 Oct 2020
DatePublished (current) - Jul 2021
Issue number1
Volume223
Number of pages20
Pages (from-to)53-72
Early online date23/10/20
Original languageEnglish

Abstract

This paper studies estimation of covariance matrices with conditional sparse structure. We overcome the challenge of estimating dense matrices using a factor structure, the challenge of estimating large-dimensional matrices by postulating sparsity on covariance of random noises, and the challenge of estimating varying matrices by allowing factor loadings to smoothly change. A kernel-weighted estimation approach combined with generalised shrinkage is proposed. Under some technical conditions, we derive uniform consistency for the developed estimation method and obtain convergence rates. Numerical studies including simulation and an empirical application are presented to examine the finite-sample performance of the developed methodology.

Bibliographical note

© 2020 Elsevier B.V. This is an author-produced version of the published paper. Uploaded in accordance with the publisher’s self-archiving policy.

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