Сравнение методов
Просматривайте выбранные методы рядом; строки с различиями подсвечены.
| Оценка методом полностью модифицированных наименьших квадратов (FMOLS)× | Оценка методом динамического обыкновенного наименьших квадратов (DOLS)× | |
|---|---|---|
| Область | Эконометрика | Эконометрика |
| Семейство | Regression model | Regression model |
| Год появления≠ | 1990 | 1993 |
| Автор метода≠ | Phillips & Hansen (time series); Pedroni (heterogeneous panels) | Stock & Watson (1993); panel extension Kao & Chiang (2001) |
| Тип | Cointegrating regression estimator | Cointegrating regression estimator |
| Основополагающий источник≠ | 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. 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 ↗ |
| Другие названия≠ | fully modified OLS, Phillips-Hansen FMOLS, Tam Düzeltilmiş OLS (FMOLS) | DOLS, Stock-Watson dynamic OLS, dynamic least squares cointegration estimator, Dinamik OLS (DOLS) |
| Связанные | 5 | 5 |
| Сводка≠ | 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. |
| ScholarGateНабор данных ↗ |
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