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) residual autocorrelations and derive their asymptotic distribution. The results are used to derive several new multivariate portmanteau tests. Simulation results show that the asymptotic standard errors are quite satisfactory compared with empirical standard errors and that the tests have reasonable empirical size and power. The distribution of the standardized residual autocorrelations is also derived.link_to_subscribed_fulltex
Since the introduction of autoregressive conditional heteroscedasticity (ARCH) by Engle, there has b...
Since the introduction of autoregressive conditional heteroscedasticity (ARCH) by Engle, there has b...
Squared-residual autocorrelations have been found useful in detecting nonlinear types of statistical...
In this article, we derive the asymptotic distribution of residual autocovariance and autocorrelatio...
Multivariate conditional heteroscedasticity models form an important class of nonlinear time series ...
The large-sample distribution of the multivariate residual autocorrelations in the vector ARMA model...
In applied time series analysis, checking for autocorrelation in a fitted model is a routine diagnos...
SUMMARY: The asymptotic distribution of residual autocorrelations for some very general nonlinear ti...
In applied time series analysis, checking for autocorrelation in a fitted model is a routine diagnos...
A new portmanteau test for time series more powerful than the tests ofLjung and Box (1978) and Monti...
This thesis aims at investigating different forms of residuals from a general time series model with...
A new portmanteau test for time series more powerful than the tests ofLjung and Box (1978) and Monti...
This study investigates the size and power properties of three multivariate tests for autocorrelatio...
The asymptotic distribution of residual autocorrelations in multivariate autoregressive index models...
In this thesis, a new univariate-multivariate portmanteau test is derived. The proposed test statist...
Since the introduction of autoregressive conditional heteroscedasticity (ARCH) by Engle, there has b...
Since the introduction of autoregressive conditional heteroscedasticity (ARCH) by Engle, there has b...
Squared-residual autocorrelations have been found useful in detecting nonlinear types of statistical...
In this article, we derive the asymptotic distribution of residual autocovariance and autocorrelatio...
Multivariate conditional heteroscedasticity models form an important class of nonlinear time series ...
The large-sample distribution of the multivariate residual autocorrelations in the vector ARMA model...
In applied time series analysis, checking for autocorrelation in a fitted model is a routine diagnos...
SUMMARY: The asymptotic distribution of residual autocorrelations for some very general nonlinear ti...
In applied time series analysis, checking for autocorrelation in a fitted model is a routine diagnos...
A new portmanteau test for time series more powerful than the tests ofLjung and Box (1978) and Monti...
This thesis aims at investigating different forms of residuals from a general time series model with...
A new portmanteau test for time series more powerful than the tests ofLjung and Box (1978) and Monti...
This study investigates the size and power properties of three multivariate tests for autocorrelatio...
The asymptotic distribution of residual autocorrelations in multivariate autoregressive index models...
In this thesis, a new univariate-multivariate portmanteau test is derived. The proposed test statist...
Since the introduction of autoregressive conditional heteroscedasticity (ARCH) by Engle, there has b...
Since the introduction of autoregressive conditional heteroscedasticity (ARCH) by Engle, there has b...
Squared-residual autocorrelations have been found useful in detecting nonlinear types of statistical...