Sammenlign metoder

	Fuldt Modificeret OLS (FMOLS) Estimator	Common Correlated Effects Mean Group (CCEMG) Estimator	Almindelig mindste kvadraters metode (OLS) regression
Fagområde	Økonometri	Økonometri	Økonometri
Familie	Regression model	Regression model	Regression model
Oprindelsesår	1990	2006	2019
Ophavsperson	Phillips & Hansen (time series); Pedroni (heterogeneous panels)	M. Hashem Pesaran	Wooldridge (textbook treatment); classical least squares
Type	Cointegrating regression estimator	Heterogeneous panel estimator	Linear regression
Oprindelig kilde	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.	Wooldridge, J. M. (2019). Introductory Econometrics: A Modern Approach (7th ed.). Cengage Learning. ISBN: 978-1337558860
Aliasser	fully modified OLS, Phillips-Hansen FMOLS, Tam Düzeltilmiş OLS (FMOLS)	common correlated effects, CCE, CCEMG, Pesaran CCE estimator	ordinary least squares, classical linear regression, linear regression, en küçük kareler regresyonu
Relaterede	5	4	5
Resumé	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.	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).