Improved multivariate portmanteau test

@article{Mahdi2012ImprovedMP,
  title={Improved multivariate portmanteau test},
  author={Esam Mahdi and A. Ian McLeod},
  journal={Journal of Time Series Analysis},
  year={2012},
  volume={33}
}
A new portmanteau diagnostic test for vector autoregressive moving average (VARMA) models that is based on the determinant of the standardized multivariate residual autocorrelations is derived. The new test statistic may be considered an extension of the univariate portmanteau test statistic suggested by Peňa and Rodríguez (2002) . The asymptotic distribution of the test statistic is derived as well as a chi‐square approximation. However, the Monte–Carlo test is recommended unless the series is… 
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References

SHOWING 1-10 OF 74 REFERENCES
The Multivariate Portmanteau Statistic
Abstract Box and Pierce have derived a goodness-of-fit test, the portmanteau test, for univariate autoregressive moving-average (ARMA) time series models. This test is here extended to multivariate
Multivariate Portmanteau Test For Autoregressive Models with Uncorrelated but Nonindependent Errors
Abstract.  We study the asymptotic behaviour of the least squares estimator, of the residual autocorrelations and of the Ljung–Box (or Box–Pierce) portmanteau test statistic for multiple
A Powerful Portmanteau Test of Lack of Fit for Time Series
A new portmanteau test for time series, more powerful than the tests of Ljung and Box and Monti, is proposed. The test is based on the mth root of the determinant of the mth autocorrelation matrix.
Improved Pena-Rodriguez portmanteau test
Portmanteau tests for ARMA models with infinite variance
Abstract.  Autoregressive and moving‐average (ARMA) models with stable Paretian errors are some of the most studied models for time series with infinite variance. Estimation methods for these models
Diagnostic checking of nonlinear multivariate time series with multivariate arch errors
Multivariate time series with multivariate ARCH errors have been found useful in many applications. In order to check the adequacy of these models, we define the sum of squared (standardized)
On testing for multivariate ARCH effects in vector time series models
Using a spectral approach, the authors propose tests to detect multivariate ARCH effects in the residuals from a multivariate regression model. The tests are based on a comparison, via a quadratic
Testing for multivariate autoregressive conditional heteroskedasticity using wavelets
On robust testing for conditional heteroscedasticity in time series models
...
...