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

Author

Listed:
  • 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)

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.

Suggested Citation

  • Guilherme Pumi & Taiane S. Prass & Sílvia R. C. Lopes, 2024. "A novel copula-based approach for parametric estimation of univariate time series through its covariance decay," Statistical Papers, Springer, vol. 65(2), pages 1041-1063, April.
    • Handle: RePEc:spr:stpapr:v:65:y:2024:i:2:d:10.1007_s00362-023-01418-z
    DOI: 10.1007/s00362-023-01418-z
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