A copula approach for dependence modeling in multivariate nonparametric time series
Marek Omelka and
Journal of Multivariate Analysis, 2019, vol. 171, issue C, 139-162
This paper is concerned with modeling the dependence structure of two (or more) time series in the presence of a (possibly multivariate) covariate which may include past values of the time series. We assume that the covariate influences only the conditional mean and the conditional variance of each of the time series but the distribution of the standardized innovations is not influenced by the covariate and is stable in time. The joint distribution of the time series is then determined by the conditional means, the conditional variances and the marginal distributions of the innovations, which we estimate nonparametrically, and the copula of the innovations, which represents the dependency structure. We consider a nonparametric and a semiparametric estimator based on the estimated residuals. We show that under suitable assumptions, these copula estimators are asymptotically equivalent to estimators that would be based on the unobserved innovations. The theoretical results are illustrated by simulations and a real data example.
Keywords: Asymptotic representation; CHARN model; Empirical copula process; Goodness-of-fit testing; Nonparametric AR-ARCH model; Nonparametric SCOMDY model; Weak convergence (search for similar items in EconPapers)
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