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Estimateur FMOLS (Fully Modified OLS)×Estimateur des Effets Communs Corrélés du Groupe Moyen (CCEMG)×Estimateur par Moindres Carrés Ordinaires Dynamiques (DOLS)×
DomaineÉconométrieÉconométrieÉconométrie
FamilleRegression modelRegression modelRegression model
Année d'origine199020061993
Auteur d'originePhillips & Hansen (time series); Pedroni (heterogeneous panels)M. Hashem PesaranStock & Watson (1993); panel extension Kao & Chiang (2001)
TypeCointegrating regression estimatorHeterogeneous panel estimatorCointegrating regression estimator
Source fondatricePhillips, P. C. B. & Hansen, B. E. (1990). Statistical Inference in Instrumental Variables Regression with I(1) Processes. Review of Economic Studies, 57(1), 99–125. DOI ↗Pesaran, M. H. (2006). Estimation and Inference in Large Heterogeneous Panels with a Multifactor Error Structure. Econometrica, 74(4), 967-1012. DOI ↗Stock, J. H. & Watson, M. W. (1993). A Simple Estimator of Cointegrating Vectors in Higher Order Integrated Systems. Econometrica, 61(4), 783–820. DOI ↗
Aliasfully modified OLS, Phillips-Hansen FMOLS, Tam Düzeltilmiş OLS (FMOLS)common correlated effects, CCE, CCEMG, Pesaran CCE estimatorDOLS, Stock-Watson dynamic OLS, dynamic least squares cointegration estimator, Dinamik OLS (DOLS)
Apparentées545
RésuméFully Modified OLS, introduced by Phillips and Hansen (1990), estimates the long-run coefficients of a cointegrating relationship among I(1) variables. It applies a semi-parametric correction to ordinary least squares to remove the bias that endogeneity and serial correlation otherwise induce in cointegrated time series or panel data.The Common Correlated Effects Mean Group estimator, introduced by Pesaran in 2006, is a heterogeneous panel-data estimator that controls for cross-sectional dependence by approximating unobserved common factors with the cross-section averages of the variables. It remains consistent when the slope coefficients differ across units.Dynamic OLS is a cointegrating-regression estimator introduced by Stock and Watson (1993) that recovers the long-run relationship between I(1) variables. It augments the static regression with leads and lags of the differenced regressors, correcting endogeneity bias parametrically so that the long-run coefficient can be estimated by ordinary least squares.
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ScholarGateComparer des méthodes: FMOLS Estimator · CCEMG Estimator · Dynamic OLS. Consulté le 2026-06-20 sur https://scholargate.app/fr/compare