A novel copula-based approach for parametric estimation of univariate time series through its covariance decay

Guilherme Pumi (), Taiane S. Prass () and Sílvia R. C. Lopes ()
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Guilherme Pumi: Programa de Pós-Graduação em Estatística-Universidade Federal do Rio Grande do Sul
Taiane S. Prass: Programa de Pós-Graduação em Estatística-Universidade Federal do Rio Grande do Sul
Sílvia R. C. Lopes: Programa de Pós-Graduação em Matemática-Universidade Federal do Rio Grande do Sul

Statistical Papers, 2024, vol. 65, issue 2, No 20, 1063 pages

Abstract: Abstract In this note we develop a new technique for parameter estimation of univariate time series by means of a parametric copula approach. The proposed methodology is based on a relationship between a process’ covariance decay and parametric bivariate copulas associated to lagged variables. This relationship provides a way for estimating parameters that are identifiable through the process’ covariance decay, such as in long range dependent processes. We provide a rigorous asymptotic theory for the proposed estimator. We also present a Monte Carlo simulation study to asses the finite sample performance of the proposed estimator.

Keywords: Copulas; Covariance decay; Time series; Parametric estimation; Primary 62M10; 62F12; Secondary 62E20 (search for similar items in EconPapers)
Date: 2024
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DOI: 10.1007/s00362-023-01418-z

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