 Robust factor modelling for high-dimensional time series: An application to air pollution dataValdério Anselmo Reisen, Adriano Marcio Sgrancio, Céline Lévy-Leduc, Pascal Bondon, Edson Zambon Monte, Higor Henrique Aranda Cotta and Flávio Augusto Ziegelmann Applied Mathematics and Computation, 2019, vol. 346, issue C, 842-852 Abstract: This paper considers the factor modelling for high-dimensional time series contaminated by additive outliers. We propose a robust variant of the estimation method given in Lam and Yao [10]. The estimator of the number of factors is obtained by an eigen analysis of a robust non-negative definite covariance matrix. Asymptotic properties of the robust eigenvalues are derived and we show that the resulting estimators have the same convergence rates as those found for the standard eigenvalues estimators. Simulations are carried out to analyse the finite sample size performance of the robust estimator of the number of factors under the scenarios of multivariate time series with and without additive outliers. As an application, the robust factor analysis is performed to reduce the dimensionality of the data and, therefore, to identify the pollution behaviour of the pollutant PM10. Keywords: Factor analysis; Time series; Robustness; Eigenvalues; Reduced rank; Air pollution (search for similar items in EconPapers) Downloads: (external link) Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:eee:apmaco:v:346:y:2019:i:c:p:842-852 DOI: 10.1016/j.amc.2018.09.062 Access Statistics for this article Applied Mathematics and Computation is currently edited by Theodore Simos More articles in Applied Mathematics and Computation from Elsevier |
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