Comptes Rendus
Statistics
A nonparametric model check for time series when the random vectors are nonstationary and absolutely regular
[Test d'un modèle non paramétrique pour des séries chronologiques lorsque les vecteurs aléatoires sont non stationnaires et absolument réguliers]
Comptes Rendus. Mathématique, Volume 354 (2016) no. 10, pp. 1042-1047.

Dans cette note, nous étudions quelques méthodes générales pour tester un modèle paramétrique associé à une série chronologique markovienne à valeurs réelles lorsque les vecteurs aléatoires sont non stationnaires et absolument réguliers. Notre idée est d'utiliser un processus empirique marqué basé sur les résidus qui converge en loi vers un processus gaussien.

In this Note, we study some general methods for testing the goodness-of-fit of a parametric model for a real-valued Markovian time series under nonstationarity and absolute regularity. For that, we define a marked empirical process based on residuals, which converges in distribution to a Gaussian process with respect to the Skorohod topology. This method was first introduced by Koul and Stute [1], and then widely developed by Ngatchou-Wandji [2,3] under more general conditions. Applications to general AR-ARCH models are given.

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DOI : 10.1016/j.crma.2016.07.014
Echarif Elharfaoui 1 ; Michel Harel 2, 3

1 LIMA, Département de mathématiques, Faculté des Sciences, Université Chouaîb-Doukkali, Maroc
2 IMT (UMR CNRS 5219), Université Paul-Sabatier, 31062 Toulouse cedex, France
3 ÉSPÉ de Limoges, 209, boulevard de Vanteaux, 87036 Limoges cedex, France
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Echarif Elharfaoui; Michel Harel. A nonparametric model check for time series when the random vectors are nonstationary and absolutely regular. Comptes Rendus. Mathématique, Volume 354 (2016) no. 10, pp. 1042-1047. doi : 10.1016/j.crma.2016.07.014. https://comptes-rendus.academie-sciences.fr/mathematique/articles/10.1016/j.crma.2016.07.014/

[1] H. Koul; W. Stute Nonparametric model checks for time series, Ann. Stat., Volume 27 (1999), pp. 204-236

[2] J. Ngatchou-Wandji Weak convergence of some marked empirical processes: application to testing heteroscedasticity, J. Nonparametr. Stat., Volume 14 (2002), pp. 325-339

[3] J. Ngatchou-Wandji Local power of a Cramer–von Mises type test for parametric autoregressive models of order one, Comput. Math. Appl., Volume 56 (2008), pp. 918-929

[4] J. Ngatchou-Wandji; M. Harel A Cramér–von Mises test for symmetry of the error distribution in asymptotically stationary stochastic models, Stat. Inference Stoch. Process., Volume 16 (2013), pp. 207-236

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