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Estimation de modèles non linéaires sur données de panel par la méthode des moments généralisés

Author

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  • Jörg Breitung
  • Michael Lechner

Abstract

[eng] GMM Estimation of Nonlinear Models on Panel Data by Jôrg Breitung and Michael Lechner . We show that the Generalized Method of Moments (GMM) methodology is a useful tool to obtain the asymptotic properties of some existing estimators for non-linear panel data models as well as to construct new ones. Many non-linear panel data models imply conditional moments, which do not depend on parameters from the off-diagonal part of the intertemporal covariance matrix of the error terms. Methods based on these moments sacrifice some efficiency compared to FTML but are much easier to compute since they do not require multivariate integration. The pooled maximum likelihood estimator, the sequential ML estimator based on minimum distance estimation in the second step, and previously suggested alternative GMM estimators are based on these moments. Although the pooled ML estimator is asymptotically the least efficient of the estimators considered, the Monte Carlo study indicates that it may have good small sample properties. We use a low dimensional approximation of the optimal instrument matrix to obtain an estimator, which appeared to be nearly as efficient as FTML. However, GMM estimators are easier to compute and also posses desirable properties. [fre] Estimation de modèles non linéaires sur données de panel par la méthode des moments généralisés . par Jorg Breitung et Michael Lechner . Nous montrons que la démarche de la méthode des moments généralisés (MGG) est un outil intéressant aussi bien pour obtenir les propriétés asymptotiques de certains estimateurs de modèles non linéaires sur données de panel que pour en construire de nouveaux. Un bon nombre de modèles de panel non linéaires impliquent des moments conditionnels, qui ne dépendent pas des covariances intertemporelles des termes d'erreurs. Les méthodes d'estimation basées sur ces moments sont moins efficaces que l'estimation r; FIML (full information maximum likelihood) mais sont plus facile à mettre en œuvre puisqu'elles ne requièrent pas d'intégration multiple. L' estimateur du maximum de vraisemblance sur données empilées, l' estimateur du maximum de vraisemblance séquentiel fondé sur un estimateur de distance minimale à la seconde étape, et les estimateurs MGG cités plus haut sont basés sur ces moments. Bien que l'estimateur MV sur données empilées soit asymptotiquement le moins efficace des estimateurs considérés, l'étude par la méthode de Monte Carlo indique qu'il peut avoir de bonnes propriétés à distance finie. Nous utilisons un ensemble, réduit d'instruments approchant l'ensemble optimal afin d'obtenir un estimateur dont l'efficacité est proche de celle de l'estimateur FIML. Cependant, les estimateurs MGG sont plus faciles à calculer et possèdent aussi les propriétés à distance finies souhaitables.

Suggested Citation

  • Jörg Breitung & Michael Lechner, 1996. "Estimation de modèles non linéaires sur données de panel par la méthode des moments généralisés," Économie et Prévision, Programme National Persée, vol. 126(5), pages 191-203.
    • Handle: RePEc:prs:ecoprv:ecop_0249-4744_1996_num_126_5_5831
    DOI: 10.3406/ecop.1996.5831
    Note: DOI:10.3406/ecop.1996.5831
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    References listed on IDEAS

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    1. Breitung, Jörg & Lechner, Michael, 1998. "Alternative GMM methods for nonlinear panel data models," SFB 373 Discussion Papers 1998,81, Humboldt University of Berlin, Interdisciplinary Research Project 373: Quantification and Simulation of Economic Processes.

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