Improved multivariate portmanteau test
- 24 August 2011
- journal article
- Published by Wiley in Journal of Time Series Analysis
- Vol. 33 (2) , 211-222
Abstract
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 byPeňa 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 very long. Extensive simulation experiments demonstrate the usefulness of this test as well as its improved power performance compared to widely used previous multivariate portmanteau diagnostic check. Two illustrative applications are given.Keywords
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This publication has 41 references indexed in Scilit:
- On testing for multivariate ARCH effects in vector time series modelsCanadian Journal of Statistics, 2008
- Portmanteau tests for ARMA models with infinite varianceJournal of Time Series Analysis, 2008
- Diagnostic checking of nonlinear multivariate time series with multivariate arch errorsJournal of Time Series Analysis, 1997
- Diagnostic Tests for Multiple Time Series ModelsThe Annals of Statistics, 1982
- The Multivariate Portmanteau StatisticJournal of the American Statistical Association, 1980
- Improved Box-Jenkins EstimatorsBiometrika, 1977
- Distribution of Residual Autocorrelations in Multiple Autoregressive SchemesJournal of the American Statistical Association, 1974
- Distribution of Residual Autocorrelations in Autoregressive-Integrated Moving Average Time Series ModelsJournal of the American Statistical Association, 1970
- Some Theorems on Matrix Differentiation with Special Reference to Kronecker Matrix ProductsJournal of the American Statistical Association, 1969
- Computing the distribution of quadratic forms in normal variablesBiometrika, 1961