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Оценител с напълно модифицирани най-малки квадрати (FMOLS)×Оценка по метода на средните групи с общи корелирани ефекти (CCEMG)×Оценител на динамични обикновени най-малки квадрати (DOLS)×
ОбластИконометрияИконометрияИконометрия
СемействоRegression modelRegression modelRegression model
Година на възникване199020061993
СъздателPhillips & Hansen (time series); Pedroni (heterogeneous panels)M. Hashem PesaranStock & Watson (1993); panel extension Kao & Chiang (2001)
ТипCointegrating regression estimatorHeterogeneous panel estimatorCointegrating regression estimator
Основополагащ източникPhillips, 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. DOI ↗Pesaran, M. H. (2006). Estimation and Inference in Large Heterogeneous Panels with a Multifactor Error Structure. Econometrica, 74(4), 967-1012. DOI ↗Stock, J. H. & Watson, M. W. (1993). A Simple Estimator of Cointegrating Vectors in Higher Order Integrated Systems. Econometrica, 61(4), 783–820. DOI ↗
Други названияfully modified OLS, Phillips-Hansen FMOLS, Tam Düzeltilmiş OLS (FMOLS)common correlated effects, CCE, CCEMG, Pesaran CCE estimatorDOLS, Stock-Watson dynamic OLS, dynamic least squares cointegration estimator, Dinamik OLS (DOLS)
Свързани545
РезюмеFully 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.
ScholarGateНабор от данни
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ScholarGateСравнение на методи: FMOLS Estimator · CCEMG Estimator · Dynamic OLS. Извлечено на 2026-06-20 от https://scholargate.app/bg/compare