So sánh phương pháp
Xem các phương pháp đã chọn cạnh nhau; những hàng khác biệt được làm nổi bật.
| Phương pháp Biến Công cụ (IV) cho Suy luận Nhân quả× | Mô hình Hiệu ứng Cố định Dữ liệu Bảng× | |
|---|---|---|
| Lĩnh vực≠ | Kinh tế học y tế | Kinh tế lượng |
| Họ≠ | Process / pipeline | Regression model |
| Năm ra đời≠ | 1990s (modern applications) | 2014 |
| Người khởi xướng≠ | Angrist & Pischke (applied econometrics); rooted in econometric theory | Hsiao (textbook treatment); within transformation of panel data |
| Loại≠ | Method | Panel data regression |
| Công trình gốc≠ | Angrist, J. D., & Pischke, J. S. (2009). Mostly Harmless Econometrics: An Empiricist's Companion. Princeton: Princeton University Press. link ↗ | Hsiao, C. (2014). Analysis of Panel Data (3rd ed.). Cambridge University Press. DOI ↗ |
| Tên gọi khác | IV, two-stage least squares, TSLS, causal estimation | fixed effects model, within estimator, panel fixed-effects regression, Panel Veri — Sabit Etkiler Modeli |
| Liên quan≠ | 3 | 5 |
| Tóm tắt≠ | Instrumental variables (IV) is an econometric method to estimate causal effects when treatment or exposure is not randomly assigned and confounding is severe or unmeasured. IV relies on a third variable (instrument) that influences treatment but does not directly affect the outcome, allowing researchers to isolate the causal effect from the noise of confounding. Developed extensively in econometrics (Angrist & Pischke, 1990s–2000s), IV methods are increasingly used in health economics and health services research to leverage natural experiments and policy changes. | The Panel Data Fixed Effects model estimates relationships from panel data (the same units observed over several time periods) while controlling for unit- and/or time-specific effects, supporting causal inference. It is developed as the within estimator in standard treatments such as Hsiao's Analysis of Panel Data (2014). |
| ScholarGateBộ dữ liệu ↗ |
|
|