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| Probit-regressionsmodel× | Instrumentalvariabel (IV) Metoden til Kausal Inferens× | |
|---|---|---|
| Fagområde≠ | Økonometri | Sundhedsøkonomi |
| Familie≠ | Regression model | Process / pipeline |
| Oprindelsesår≠ | 2018 | 1990s (modern applications) |
| Ophavsperson≠ | Greene (textbook treatment); classical discrete-choice modelling | Angrist & Pischke (applied econometrics); rooted in econometric theory |
| Type≠ | Binary discrete-choice model | Method |
| Oprindelig kilde≠ | Greene, W. H. (2018). Econometric Analysis (8th ed.). Pearson. ISBN: 978-0134461366 | Angrist, J. D., & Pischke, J. S. (2009). Mostly Harmless Econometrics: An Empiricist's Companion. Princeton: Princeton University Press. link ↗ |
| Aliasser≠ | probit regression, normit model, Probit Modeli | IV, two-stage least squares, TSLS, causal estimation |
| Relaterede≠ | 5 | 3 |
| Resumé≠ | The probit model is a regression method for a binary (0/1) outcome that maps a linear index of the predictors through the standard normal cumulative distribution function to produce a probability. It is a classical discrete-choice alternative to logistic regression, developed in standard econometrics treatments such as Greene's Econometric Analysis (2018). | 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. |
| ScholarGateDatasæt ↗ |
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