Nonparametric approach for non-Gaussian vector stationary processes
Journal of Multivariate Analysis
Time series: data analysis and theory
Time series: data analysis and theory
On consistent testing for serial correlation of unknown form in vector time series models
Journal of Multivariate Analysis
A local spectral approach for assessing time series model misspecification
Journal of Multivariate Analysis
Topology Selection in Graphical Models of Autoregressive Processes
The Journal of Machine Learning Research
A note on testing hypotheses for stationary processes in the frequency domain
Journal of Multivariate Analysis
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We propose a general nonparametric approach for testing hypotheses about the spectral density matrix of multivariate stationary time series based on estimating the integrated deviation from the null hypothesis. This approach covers many important examples from interrelation analysis such as tests for noncorrelation or partial noncorrelation. Based on a central limit theorem for integrated quadratic functionals of the spectral matrix, we derive asymptotic normality of a suitably standardized version of the test statistic under the null hypothesis and under fixed as well as under sequences of local alternatives. The results are extended to cover also parametric and semiparametric hypotheses about spectral density matrices, which includes as examples goodness-of-fit tests and tests for separability.