 Vine copula Granger causality in meanHyuna Jang, Jong-Min Kim and Hohsuk Noh Economic Modelling, 2022, vol. 109, issue C Abstract: Ever since the Granger causality test was proposed in 1969, financial market researchers have used it heavily to determine whether the past of a one-time series facilitates the future prediction of another time series. However, as many researchers have noted, the traditional Granger causality test based on the vector autoregression model has limitations in detecting nonlinear causality. To relax the parametric model assumptions of the Granger causality test, nonparametric versions have been proposed to use the advantage of detecting nonlinear Granger causality but have shown difficulty in selecting smoothing parameters that significantly affect detection performance. To overcome the difficulties of both parametric and nonparametric Granger causality tests, we propose the vine copula Granger causality test in mean based on the semiparametric time-series modeling technique. The proposed test overcomes the shortcomings of parametric modeling and has a computational advantage over the nonparametric tests. Our test shows good size and power performance with various simulated data and a real data. Keywords: Granger causality; Multivariate time series; Semiparametric modeling; Stationary vine copula models (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:ecmode:v:109:y:2022:i:c:s026499932200044x DOI: 10.1016/j.econmod.2022.105798 Access Statistics for this article Economic Modelling is currently edited by S. Hall and P. Pauly More articles in Economic Modelling from Elsevier |
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