Comparar métodos
Revisa los métodos seleccionados uno junto a otro; las filas que difieren aparecen resaltadas.
| Modelo de Efectos Aleatorios para Datos de Panel× | Método de Variables Instrumentales (VI) para Inferencia Causal× | |
|---|---|---|
| Campo≠ | Econometría | Economía de la salud |
| Familia≠ | Regression model | Process / pipeline |
| Año de origen≠ | 2021 | 1990s (modern applications) |
| Autor original≠ | Baltagi (textbook treatment); classical random-effects panel estimator | Angrist & Pischke (applied econometrics); rooted in econometric theory |
| Tipo≠ | Panel data regression | Method |
| Fuente seminal≠ | Baltagi, B. H. (2021). Econometric Analysis of Panel Data (6th ed.). Springer. DOI ↗ | Angrist, J. D., & Pischke, J. S. (2009). Mostly Harmless Econometrics: An Empiricist's Companion. Princeton: Princeton University Press. link ↗ |
| Alias | random effects panel model, RE estimator, GLS random effects, Panel Veri — Rassal Etkiler Modeli | IV, two-stage least squares, TSLS, causal estimation |
| Relacionados≠ | 5 | 3 |
| Resumen≠ | The Random Effects model is a panel-data regression that treats unobserved individual heterogeneity as a random component drawn from a common distribution, rather than a separate parameter for each unit. It is a standard estimator in panel econometrics, developed in textbook treatments such as Baltagi's Econometric Analysis of Panel Data (2021). | 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. |
| ScholarGateConjunto de datos ↗ |
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