Robust estimation for the covariance matrix of multivariate time series based on normal mixtures

Byungsoo Kim and Sangyeol Lee

Computational Statistics & Data Analysis, 2013, vol. 57, issue 1, 125-140

Abstract: In this paper, we study the robust estimation for the covariance matrix of stationary multivariate time series. As a robust estimator, we propose to use a minimum density power divergence estimator (MDPDE) designed by Basu et al. (1998). To supplement the result of Kim and Lee (2011), we employ a multivariate normal mixture family instead of a multivariate normal family. As a special case, we consider the robust estimator for the autocovariance function of univariate stationary time series. It is shown that the MDPDE is strongly consistent and asymptotically normal under regularity conditions. Simulation results are provided for illustration. A real data analysis applied to the portfolio selection problem is also considered.

Keywords: Density-based divergence measures; Robust estimation; Autocovariance function; Consistency; Asymptotic normality (search for similar items in EconPapers)
Date: 2013
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Citations: View citations in EconPapers (4)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:csdana:v:57:y:2013:i:1:p:125-140

DOI: 10.1016/j.csda.2012.06.012

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