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| Modello Probit di Regressione× | Metodo delle Variabili Strumentali (IV) per l'Inferenza Causale× | |
|---|---|---|
| Campo≠ | Econometria | Economia sanitaria |
| Famiglia≠ | Regression model | Process / pipeline |
| Anno di origine≠ | 2018 | 1990s (modern applications) |
| Ideatore≠ | Greene (textbook treatment); classical discrete-choice modelling | Angrist & Pischke (applied econometrics); rooted in econometric theory |
| Tipo≠ | Binary discrete-choice model | Method |
| Fonte seminale≠ | 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 ↗ |
| Alias≠ | probit regression, normit model, Probit Modeli | IV, two-stage least squares, TSLS, causal estimation |
| Correlati≠ | 5 | 3 |
| Sintesi≠ | 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. |
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