Võrdle meetodeid

	Täielikult modifitseeritud OLS (FMOLS) estimaator	Dünaamiline tavaliste vähimate ruutude (DOLS) estimaator	Tavaline vähimruutude (OLS) regressioon
Valdkond	Ökonomeetria	Ökonomeetria	Ökonomeetria
Perekond	Regression model	Regression model	Regression model
Tekkeaasta	1990	1993	2019
Looja	Phillips & Hansen (time series); Pedroni (heterogeneous panels)	Stock & Watson (1993); panel extension Kao & Chiang (2001)	Wooldridge (textbook treatment); classical least squares
Tüüp	Cointegrating regression estimator	Cointegrating regression estimator	Linear regression
Algallikas	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.	Stock, J. H. & Watson, M. W. (1993). A Simple Estimator of Cointegrating Vectors in Higher Order Integrated Systems. Econometrica, 61(4), 783–820.	Wooldridge, J. M. (2019). Introductory Econometrics: A Modern Approach (7th ed.). Cengage Learning. ISBN: 978-1337558860
Rööpnimetused	fully modified OLS, Phillips-Hansen FMOLS, Tam Düzeltilmiş OLS (FMOLS)	DOLS, Stock-Watson dynamic OLS, dynamic least squares cointegration estimator, Dinamik OLS (DOLS)	ordinary least squares, classical linear regression, linear regression, en küçük kareler regresyonu
Seotud	5	5	5
Kokkuvõte	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.	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.	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).