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

  • Jörg Breitung
  • Michael Lechner

[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.

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Article provided by Programme National Persée in its journal Économie & prévision.

Volume (Year): 126 (1996)
Issue (Month): 5 ()
Pages: 191-203

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Handle: RePEc:prs:ecoprv:ecop_0249-4744_1996_num_126_5_5831
Note: DOI:10.3406/ecop.1996.5831
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  1. Chamberlain, Gary, 1984. "Panel data," Handbook of Econometrics, in: Z. Griliches† & M. D. Intriligator (ed.), Handbook of Econometrics, edition 1, volume 2, chapter 22, pages 1247-1318 Elsevier.
  2. Nerlove, Marc, 1971. "Further Evidence on the Estimation of Dynamic Economic Relations from a Time Series of Cross Sections," Econometrica, Econometric Society, vol. 39(2), pages 359-82, March.
  3. Keane, Michael P, 1994. "A Computationally Practical Simulation Estimator for Panel Data," Econometrica, Econometric Society, vol. 62(1), pages 95-116, January.
  4. Holtz-Eakin, Douglas & Newey, Whitney & Rosen, Harvey S, 1988. "Estimating Vector Autoregressions with Panel Data," Econometrica, Econometric Society, vol. 56(6), pages 1371-95, November.
  5. Butler, J S & Moffitt, Robert, 1982. "A Computationally Efficient Quadrature Procedure for the One-Factor Multinomial Probit Model," Econometrica, Econometric Society, vol. 50(3), pages 761-64, May.
  6. Kodde, D A & Palm, Franz C & Pfann, G A, 1990. "Asymptotic Least-Squares Estimation Efficiency Considerations and Applications," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 5(3), pages 229-43, July-Sept.
  7. Avery, Robert B & Hansen, Lars Peter & Hotz, V Joseph, 1983. "Multiperiod Probit Models and Orthogonality Condition Estimation," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 24(1), pages 21-35, February.
  8. Arellano, Manuel & Bond, Stephen, 1991. "Some Tests of Specification for Panel Data: Monte Carlo Evidence and an Application to Employment Equations," Review of Economic Studies, Wiley Blackwell, vol. 58(2), pages 277-97, April.
  9. Guilkey, David K. & Murphy, James L., 1993. "Estimation and testing in the random effects probit model," Journal of Econometrics, Elsevier, vol. 59(3), pages 301-317, October.
  10. Chamberlain, Gary, 1980. "Analysis of Covariance with Qualitative Data," Review of Economic Studies, Wiley Blackwell, vol. 47(1), pages 225-38, January.
  11. Lechner, Michael, 1995. "Some Specification Tests for Probit Models Estimated on Panel Data," Journal of Business & Economic Statistics, American Statistical Association, vol. 13(4), pages 475-88, October.
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