Vertaile menetelmiä

	Dynamic OLS	Common Correlated Effects Mean Group (CCEMG) -estimaattori	OLS-regressio (Ordinary Least Squares)
Tieteenala	Ekonometria	Ekonometria	Ekonometria
Menetelmäperhe	Regression model	Regression model	Regression model
Syntyvuosi	1993	2006	2019
Kehittäjä	Stock & Watson (1993); panel extension Kao & Chiang (2001)	M. Hashem Pesaran	Wooldridge (textbook treatment); classical least squares
Tyyppi	Cointegrating regression estimator	Heterogeneous panel estimator	Linear regression
Alkuperäislähde	Stock, J. H. & Watson, M. W. (1993). A Simple Estimator of Cointegrating Vectors in Higher Order Integrated Systems. Econometrica, 61(4), 783–820.	Pesaran, M. H. (2006). Estimation and Inference in Large Heterogeneous Panels with a Multifactor Error Structure. Econometrica, 74(4), 967-1012.	Wooldridge, J. M. (2019). Introductory Econometrics: A Modern Approach (7th ed.). Cengage Learning. ISBN: 978-1337558860
Rinnakkaisnimet	DOLS, Stock-Watson dynamic OLS, dynamic least squares cointegration estimator, Dinamik OLS (DOLS)	common correlated effects, CCE, CCEMG, Pesaran CCE estimator	ordinary least squares, classical linear regression, linear regression, en küçük kareler regresyonu
Liittyvät	5	4	5
Tiivistelmä	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.	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.	Ordinary Least Squares is the classical linear regression method that explains a continuous outcome as a linear combination of predictors. It estimates the coefficients by minimising the sum of squared residuals, and under the Gauss-Markov assumptions these estimates are the best linear unbiased estimator (BLUE).