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Journal of Time Series Analysis 28 (2007) 454-470

Multivariate Portmanteau test for Autoregressive models with uncorrelated but nonindependent errors

Christian Francq 1, Hamdi Raissi 2

(01/06/2007)

In this paper we consider estimation and test of fit for multiple autoregressive time series models with nonindependent innovations. We derive the asymptotic distribution of the residual autocorrelations. It is shown that this asymptotic distribution can be quite different for models with iid innovations and models in which the innovations exhibit conditional heteroscedasticity or other forms of dependence. Consequently, the usual chi-square distribution does not provide adequate approximation to the distribution of the Box-Pierce goodness-of-fit portmanteau test in the presence of nonindependent innovations. We then propose a method to adjust the critical values of the portmanteau tests. Monte Carlo experiments illustrate the finite sample performance of the modified portmanteau test.

1 :	ECONOMIE QUANTITATIVE, INTEGRATION, POLITIQUES PUBLIQUES ET ECONOMETRIE (EQUIPPE)
	Université Lille III - Sciences humaines et sociales
2 :	Institut de Recherche Mathématique de Rennes (IRMAR)
	CNRS : UMR6625 – Université de Rennes 1 – École normale supérieure de Cachan - ENS Cachan – Institut National des Sciences Appliquées (INSA) : - RENNES – Université de Rennes II - Haute Bretagne

Domaine

:

Statistiques/Méthodologie Statistiques/Applications

Mots Clés : Vector weak AR model – Goodness-of-fit test – Residual autocorrelation – Diagnostic Checking – Box-Pierce and Ljung-Box portmanteau tests.

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Contributeur : Hamdi Raissi <>

Soumis le : Lundi 13 Septembre 2010, 21:20:19

Dernière modification le : Vendredi 19 Novembre 2010, 15:21:22