Time series with infinite-order partial copula dependence

Bladt Martin () and McNeil Alexander J. ()
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Bladt Martin: Faculty of Business and Economics, University of Lausanne, 1015 Lausanne, Switzerland
McNeil Alexander J.: The University of York Management School, University of York, Heslington, York YO10 5DD, United Kingdom

Dependence Modeling, 2022, vol. 10, issue 1, 87-107

Abstract: Stationary and ergodic time series can be constructed using an s-vine decomposition based on sets of bivariate copula functions. The extension of such processes to infinite copula sequences is considered and shown to yield a rich class of models that generalizes Gaussian ARMA and ARFIMA processes to allow both non-Gaussian marginal behaviour and a non-Gaussian description of the serial partial dependence structure. Extensions of classical causal and invertible representations of linear processes to general s-vine processes are proposed and investigated. A practical and parsimonious method for parameterizing s-vine processes using the Kendall partial autocorrelation function is developed. The potential of the resulting models to give improved statistical fits in many applications is indicated with an example using macroeconomic data.

Keywords: time series; vine copulas; Gaussian processes; ARMA processes; ARFIMA processes; 62M10; 62M05; 62H05; 60G10; 60G15; 60G22 (search for similar items in EconPapers)
Date: 2022
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Persistent link: https://EconPapers.repec.org/RePEc:vrs:demode:v:10:y:2022:i:1:p:87-107:n:2

DOI: 10.1515/demo-2022-0105

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