Uporedite metode

	Динамички Обични Најмањи Квадрати (DOLS) Естиматор	Augmented Mean Group (AMG) estimator	Regresija običnih najmanjih kvadrata (OLS)
Oblast	Ekonometrija	Ekonometrija	Ekonometrija
Porodica	Regression model	Regression model	Regression model
Godina nastanka	1993	2010	2019
Tvorac	Stock & Watson (1993); panel extension Kao & Chiang (2001)	Eberhardt & Teal; Bond & Eberhardt	Wooldridge (textbook treatment); classical least squares
Tip	Cointegrating regression estimator	Heterogeneous panel data estimator	Linear regression
Temeljni izvor	Stock, J. H. & Watson, M. W. (1993). A Simple Estimator of Cointegrating Vectors in Higher Order Integrated Systems. Econometrica, 61(4), 783–820.	Eberhardt, M. & Teal, F. (2010). Productivity Analysis in Global Manufacturing Production. Economics Series Working Papers, No. 515, University of Oxford.	Wooldridge, J. M. (2019). Introductory Econometrics: A Modern Approach (7th ed.). Cengage Learning. ISBN: 978-1337558860
Drugi nazivi	DOLS, Stock-Watson dynamic OLS, dynamic least squares cointegration estimator, Dinamik OLS (DOLS)	AMG estimator, augmented mean group, Artırılmış Ortalama Grup Tahmincisi (AMG)	ordinary least squares, classical linear regression, linear regression, en küçük kareler regresyonu
Srodne	5	4	5
Sažetak	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 Augmented Mean Group estimator, developed by Eberhardt and Teal (2010), is a panel data method for estimating heterogeneous slope coefficients in the presence of cross-sectional dependence. It approximates the unobserved common dynamic process driving all units and folds it into unit-by-unit regressions, then averages the results.	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).