Copula directional dependence of discrete time series marginals

Mohammed Alqawba, Norou Diawara, Jong Min Kim

Research output: Contribution to journalArticlepeer-review

2 Scopus citations


To understand the dynamic relationship of discrete time series processes, we adopted copula directional dependence via beta regression model applied with generalized autoregressive conditional heteroscedasticity (INGARCH) marginals. To validate the proposed method, we completed simulations of two INGARCH processes from asymmetric bivariate copula function with members such as Gaussian and Plackett copula functions. The simulations show that the proposed method is consistent for deriving directional dependent measurements regardless of the choice of the symmetric members. The proposed method is applied to the bivariate discrete time series data of the monthly counts of sandstorms and dust haze phenomena in Saudi Arabia.

Original languageEnglish (US)
Pages (from-to)3733-3750
Number of pages18
JournalCommunications in Statistics: Simulation and Computation
Issue number11
StatePublished - 2021

Bibliographical note

Funding Information:
The authors thank the Editor and the anonymous referees for the constructive comments, which led to considerable improvement of the manuscript. The authors thank the Saudi General Authority of Meteorological & Environmental Protection for making the sandstorm data available.

Publisher Copyright:
© 2019 Taylor & Francis Group, LLC.


  • Beta regression
  • Count time series
  • Directional dependence
  • GLM
  • copula


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