On testing for separable correlations of multivariate time series

DOI10.1111/j.1467-9892.2004.01795.xzbMath1062.62196OpenAlexW3125905052MaRDI QIDQ4677027

Yoshihiro Yajima, Yasumasa Matsuda

Publication date: 20 May 2005

Published in: Journal of Time Series Analysis (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1111/j.1467-9892.2004.01795.x

zbMATH Keywords

asymptotic distributionperiodogramnonparametric inference

Mathematics Subject Classification ID

Inference from spatial processes (62M30) Nonparametric hypothesis testing (62G10) Time series, auto-correlation, regression, etc. in statistics (GARCH) (62M10) Asymptotic distribution theory in statistics (62E20)

Related Items (10)

Selecting models with different spectral density matrix structures by the cross-validated log likelihood criterion ⋮ Bootstrapping Frequency Domain Tests in Multivariate Time Series with an Application to Comparing Spectral Densities ⋮ Quantifying deviations from separability in space-time functional processes ⋮ Statistical analysis of a spatio-temporal model with location-dependent parameters and a test for spatial stationarity ⋮ Inference and testing breaks in large dynamic panels with strong cross sectional dependence ⋮ Testing nonparametric and semiparametric hypotheses in vector stationary processes ⋮ Inference without smoothing for large panels with cross-sectional and temporal dependence ⋮ A tuning parameter free test for properties of space-time covariance functions ⋮ Testing non-parametric hypotheses for stationary processes by estimating minimal distances ⋮ On nonparametric and semiparametric testing for multivariate linear time series

Cites Work

• A limit theory for long-range dependence and statistical inference on related models
• Nonparametric approach for non-Gaussian vector stationary processes
• Statistics for Spatial Data
• Unnamed Item

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