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New HSIC-based tests for independence between two stationary multivariate time series
This paper proposes some novel one-sided omnibus tests for independence ...
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A Powerful Portmanteau Test for Detecting Nonlinearity in Time Series
A new portmanteau test statistic is proposed for detecting nonlinearity ...
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Quantile-Frequency Analysis and Spectral Divergence Metrics for Diagnostic Checks of Time Series With Nonlinear Dynamics
Nonlinear dynamic volatility has been observed in many financial time se...
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On testing for high-dimensional white noise
Testing for white noise is a classical yet important problem in statisti...
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Assessing the reliability of ensemble forecasting systems under serial dependence
The problem of testing the reliability of ensemble forecasting systems i...
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Testing Simultaneous Diagonalizability
This paper proposes novel methods to test for simultaneous diagonalizati...
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Max-sum tests for cross-sectional dependence of high-demensional panel data
We consider a testing problem for cross-sectional dependence for high-di...
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Simultaneous Diagnostic Testing for Linear-Nonlinear Dependence in Time Series
Several goodness-of-fit tests have been proposed to detect linearity in stationary time series based on the autocorrelations of the residuals. Others have been developed based on the autocorrelations of the square residuals or based on the cross-correlations between residuals and their squares to test for nonlinearity. In this paper, we propose omnibus portmanteau tests that can be used for detecting, simultaneously, many linear, bilinear, and nonlinear dependence structures in stationary time series based on combining all these correlations. An extensive simulation study is conducted to examine the finite sample performance of the proposed tests. The simulation results show that the proposed tests successfully control the Type I error probability and tend to be more powerful than other tests in most cases. The efficacy of the proposed tests is demonstrated through the analysis of Amazon.com, Inc., daily log-returns.
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