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	Fullt Modifierad OLS (FMOLS)-estimator	Common Correlated Effects Mean Group (CCEMG) skattare	Dynamisk OLS-estimator (Dynamic Ordinary Least Squares, DOLS)
Ämnesområde	Ekonometri	Ekonometri	Ekonometri
Familj	Regression model	Regression model	Regression model
Ursprungsår	1990	2006	1993
Upphovsperson	Phillips & Hansen (time series); Pedroni (heterogeneous panels)	M. Hashem Pesaran	Stock & Watson (1993); panel extension Kao & Chiang (2001)
Typ	Cointegrating regression estimator	Heterogeneous panel estimator	Cointegrating regression estimator
Ursprungskälla	Phillips, 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.	Pesaran, M. H. (2006). Estimation and Inference in Large Heterogeneous Panels with a Multifactor Error Structure. Econometrica, 74(4), 967-1012.	Stock, J. H. & Watson, M. W. (1993). A Simple Estimator of Cointegrating Vectors in Higher Order Integrated Systems. Econometrica, 61(4), 783–820.
Alias	fully modified OLS, Phillips-Hansen FMOLS, Tam Düzeltilmiş OLS (FMOLS)	common correlated effects, CCE, CCEMG, Pesaran CCE estimator	DOLS, Stock-Watson dynamic OLS, dynamic least squares cointegration estimator, Dinamik OLS (DOLS)
Närliggande	5	4	5
Sammanfattning	Fully 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.