Improved multivariate portmanteau test

  title={Improved multivariate portmanteau test},
  author={Esam Mahdi and A. Ian McLeod},
  journal={Journal of Time Series Analysis},
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… 
Kernel-based portmanteau diagnostic test for ARMA time series models
In this paper, the definition of the Toeplitz autocorrelation matrix is used to derive a kernel-based portmanteau test statistic for ARMA models. Under the null hypothesis of no serial correlation,
A Powerful Portmanteau Test for Detecting Nonlinearity in Time Series
A new portmanteau test statistic is proposed for detecting nonlinearity in time series data. In this paper, we elaborate on the Toeplitz autocorrelation matrix to the autocorrelation and
Advances in Portmanteau Diagnostic Tests
Portmanteau test serves an important role in model diagnostics for Box-Jenkins Modelling procedures. A large number of portmanteau test based on the autocorrelation function are proposed for a
portes: An R Package for Portmanteau Tests in Time Series Models
The asymptotic distributions and the Monte Carlo procedures of the most popular univariate and multivariate portmanteau test statistics, including a new generalized variance statistic, are implemented in the R package portes with extensive illustrative applications.
A Cauchy estimator test for autocorrelation
This article presents a new test for serial correlation in an observed stationary time series. Rather than using the traditional portmanteau tests based on the sample autocorrelation function, we
Comparative study of portmanteau tests for the residuals autocorrelation in ARMA models
The portmanteau statistic for testing the adequacy of an autoregressive moving average (ARMA) model is based on the first m autocorrelations of the residuals from the fitted model. We consider some
Recent work in the literature has shown weighted variants of the classic portmanteau test for time series can be more powerful in many situations. In this article, we study the asymptotic
Some weighted mixed portmanteau tests for diagnostic checking in linear time series models
It is found that the weighted mixed tests outperform when higher order ARMA models are fitted and diagnostic checks are performed via testing lack of residual autocorrelations.
Improved functional portmanteau tests
ABSTRACT Functional time series is a popular method of forecasting in functional data analysis. The Box-Jenkins methodology for model building, with the aim of forecasting, includes three iterative
New Goodness-of-Fit Tests for Time Series Models
This article proposes omnibus portmanteau tests for contrasting adequacy of time series models. The test statistics are based on combining the autocorrelation function of the conditional residuals,


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