Consistent testing for a constant copula under strong mixing based on the tapered block multiplier technique

DOI10.1016/j.jmva.2012.12.002zbMath1277.62207arXiv1206.1675OpenAlexW1975161230MaRDI QIDQ391536

Martin Ruppert, Axel Bücher

Publication date: 10 January 2014

Published in: Journal of Multivariate Analysis (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/1206.1675

zbMATH Keywords

time seriesstrong mixingnonparametric estimationchange point testmultiplier central limit theoremempirical copula process

Mathematics Subject Classification ID

Nonparametric regression and quantile regression (62G08) Nonparametric hypothesis testing (62G10) Time series, auto-correlation, regression, etc. in statistics (GARCH) (62M10) Central limit and other weak theorems (60F05) Characterization and structure theory for multivariate probability distributions; copulas (62H05) Monte Carlo methods (65C05)

Related Items (16)

A dependent multiplier bootstrap for the sequential empirical copula process under strong mixing ⋮ A general framework for testing homogeneity hypotheses about copulas ⋮ A fluctuation test for constant Spearman's rho with nuisance-free limit distribution ⋮ Testing the constancy of Spearman's rho in multivariate time series ⋮ Testing and dating structural changes in copula-based dependence measures ⋮ Empirical and sequential empirical copula processes under serial dependence ⋮ Consistent nonparametric tests for detecting gradual changes in the marginals and the copula of multivariate time series ⋮ Dependent multiplier bootstraps for non-degenerate \(U\)-statistics under mixing conditions with applications ⋮ A general approach to the joint asymptotic analysis of statistics from sub-samples ⋮ GOODNESS-OF-FIT TESTS FOR MULTIVARIATE COPULA-BASED TIME SERIES MODELS ⋮ Nonparametric tests for tail monotonicity ⋮ Nonparametric tests for constant tail dependence with an application to energy and finance ⋮ Testing for structural breaks in factor copula models ⋮ Strong approximation of multidimensional \(\mathbb P\)-\(\mathbb P\) plots processes by Gaussian processes with applications to statistical tests ⋮ Detecting changes in cross-sectional dependence in multivariate time series ⋮ A new family of copula-based concordance orderings of random pairs: properties and nonparametric tests

Uses Software

• TwoCop

Cites Work

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