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| Hálózatökonometria (társhatások)× | Instrumentális Változók (IV) Módszer Kauzális Infláció Becslésére× | |
|---|---|---|
| Tudományterület≠ | Ökonometria | Egészség-gazdaságtan |
| Módszercsalád≠ | Regression model | Process / pipeline |
| Keletkezés éve≠ | 2009 | 1990s (modern applications) |
| Megalkotó≠ | Yann Bramoullé, Habiba Djebbari & Bernard Fortin | Angrist & Pischke (applied econometrics); rooted in econometric theory |
| Típus≠ | Linear-in-means peer effects regression | Method |
| Alapmű≠ | Bramoullé, Y., Djebbari, H., & Fortin, B. (2009). Identification of peer effects through social networks. Journal of Econometrics, 150(1), 41–55. DOI ↗ | Angrist, J. D., & Pischke, J. S. (2009). Mostly Harmless Econometrics: An Empiricist's Companion. Princeton: Princeton University Press. link ↗ |
| Alternatív nevek | Social Interactions Model, Peer Effects Model, Social Network Regression, Ağ Ekonometrisi | IV, two-stage least squares, TSLS, causal estimation |
| Kapcsolódó | 3 | 3 |
| Összefoglaló≠ | Network econometrics estimates how individuals' outcomes are causally shaped by the behaviour and characteristics of their social-network neighbours. Formalised by Bramoullé, Djebbari, and Fortin (2009), the framework embeds a row-normalised adjacency matrix into a linear regression, separating endogenous peer effects (imitation of outcomes), exogenous contextual effects (influence of neighbours' attributes), and correlated effects (shared environment), while using network topology to construct valid instruments. | 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. |
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