This package contains functions for the analysis of time series using copula models. A full description of a strictly stationary time series can be obtained by choosing a univariate marginal distribution and a time series copula process, i.e. a serially dependent process of uniform random variables. Examples of the latter are the copula processes of Gaussian ARMA models and D-vine copula processes. Methods are provided for simulation, estimation and forecasting of time series copula models.
McNeil, A.J. (2021). Modelling volatile time series with v-transforms and copulas. Risks, 9(14). https://www.mdpi.com/2227-9091/9/1/14
Bladt, M., & McNeil, A.J. (2021). Time series copula models using d-vines and v-transforms. Econometrics and Statistics. https://www.sciencedirect.com/science/article/pii/S2452306221000800
Bladt, M., & McNeil, A.J. (2021). Time series models with infinite-order partial copula dependence. https://arxiv.org/abs/2107.00960
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