Salīdzināt metodes
Apskatiet izvēlētās metodes blakus; rindas, kas atšķiras, ir izceltas.
| DOLS (Dynamic Ordinary Least Squares) novērtēšanas rīks× | Augmented Mean Group (AMG) novērtētājs× | |
|---|---|---|
| Nozare | Ekonometrija | Ekonometrija |
| Saime | Regression model | Regression model |
| Izcelsmes gads≠ | 1993 | 2010 |
| Autors≠ | Stock & Watson (1993); panel extension Kao & Chiang (2001) | Eberhardt & Teal; Bond & Eberhardt |
| Tips≠ | Cointegrating regression estimator | Heterogeneous panel data estimator |
| Pirmavots≠ | Stock, J. H. & Watson, M. W. (1993). A Simple Estimator of Cointegrating Vectors in Higher Order Integrated Systems. Econometrica, 61(4), 783–820. DOI ↗ | Eberhardt, M. & Teal, F. (2010). Productivity Analysis in Global Manufacturing Production. Economics Series Working Papers, No. 515, University of Oxford. link ↗ |
| Citi nosaukumi≠ | DOLS, Stock-Watson dynamic OLS, dynamic least squares cointegration estimator, Dinamik OLS (DOLS) | AMG estimator, augmented mean group, Artırılmış Ortalama Grup Tahmincisi (AMG) |
| Saistītās≠ | 5 | 4 |
| Kopsavilkums≠ | 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. |
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