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

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 relations...

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Bibliographic Details
Published inStatistical papers (Berlin, Germany) Vol. 65; no. 2; pp. 1041 - 1063
Main Authors Pumi, Guilherme, Prass, Taiane S., Lopes, Sílvia R. C.
Format Journal Article
LanguageEnglish
Published Berlin/Heidelberg Springer Berlin Heidelberg 01.04.2024
Springer Nature B.V
Subjects
Bivariate analysis
Covariance
Decay
Economic Theory/Quantitative Economics/Mathematical Methods
Economics
Finance
Insurance
Management
Markov analysis
Mathematics and Statistics
Monte Carlo simulation
Operations Research/Decision Theory
Parameter estimation
Parameter identification
Probability Theory and Stochastic Processes
Random variables
Regular Article
Statistics
Statistics for Business
Time series
Primary 62M10
Copulas
Secondary 62E20
Time series
62F12
Parametric estimation
Covariance decay
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Summary: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.
ISSN:0932-5026
1613-9798
DOI:10.1007/s00362-023-01418-z