So sánh phương pháp
Xem các phương pháp đã chọn cạnh nhau; những hàng khác biệt được làm nổi bật.
| Ước lượng Khoảng cách (Between Estimator) cho Dữ liệu Bảng× | Bình phương nhỏ nhất gộp (Pooled Ordinary Least Squares) cho dữ liệu bảng× | |
|---|---|---|
| Lĩnh vực | Kinh tế lượng | Kinh tế lượng |
| Họ | Regression model | Regression model |
| Năm ra đời≠ | 2008 | 2010 |
| Người khởi xướng≠ | Badi Baltagi (treatment) | Jeffrey Wooldridge (treatment) |
| Loại≠ | OLS on group means | Linear regression on stacked panel observations |
| Công trình gốc≠ | Baltagi, B. H. (2008). Econometric Analysis of Panel Data (4th ed.). John Wiley & Sons. ISBN: 978-0-470-51886-1 | Wooldridge, J. M. (2010). Econometric Analysis of Cross Section and Panel Data (2nd ed.). MIT Press. ISBN: 978-0-262-23258-8 |
| Tên gọi khác | Between-Groups Estimator, Cross-Sectional Averages Estimator, Panel Between Estimator, Gruplar-Arası Tahmin Edici | Pooled OLS, Pooled Ordinary Least Squares, Simple Panel OLS, Havuzlanmış EKK |
| Liên quan | 2 | 2 |
| Tóm tắt≠ | The Between Estimator is a panel data regression technique that identifies regression coefficients exclusively from cross-sectional variation across individuals, by collapsing the panel to individual-specific time-averaged observations and applying ordinary least squares to those group means. It is used in economics, sociology, and political science when researchers are interested in long-run or structural differences between units rather than short-run within-unit dynamics. | Pooled OLS applies standard ordinary least squares to panel data by stacking all cross-sectional and time observations into a single dataset and ignoring the panel structure during estimation. It is the most transparent starting point for panel data analysis, widely used in economics, finance, and social sciences when researchers wish to estimate average partial effects across individuals and time periods without imposing strong distributional assumptions about unobserved heterogeneity. |
| ScholarGateBộ dữ liệu ↗ |
|
|