Methoden vergleichen

	Probit-Regressionsmodell	Logistische Regression	Paneldaten-Fixed-Effects-Modell
Fachgebiet	Ökonometrie	Forschungsstatistik	Ökonometrie
Familie	Regression model	Process / pipeline	Regression model
Entstehungsjahr	2018	1958	2014
Urheber	Greene (textbook treatment); classical discrete-choice modelling	David Roxbee Cox	Hsiao (textbook treatment); within transformation of panel data
Typ	Binary discrete-choice model	Method	Panel data regression
Wegweisende Quelle	Greene, W. H. (2018). Econometric Analysis (8th ed.). Pearson. ISBN: 978-0134461366	Cox, D. R. (1958). The regression analysis of binary sequences. Journal of the Royal Statistical Society, Series B, 20(2), 215–242.	Hsiao, C. (2014). Analysis of Panel Data (3rd ed.). Cambridge University Press.
Aliasnamen	probit regression, normit model, Probit Modeli	logit model, binomial logistic regression, LR	fixed effects model, within estimator, panel fixed-effects regression, Panel Veri — Sabit Etkiler Modeli
Verwandt	5	3	5
Zusammenfassung	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).	Logistic regression is a statistical method for modeling the probability of a binary outcome (disease present/absent, success/failure) as a function of continuous and categorical predictors. Developed by David Roxbee Cox (1958), it solves the problem of predicting categorical outcomes by applying a logistic transformation to constrain predictions to the [0,1] probability interval, enabling accurate risk stratification, diagnostic prediction, and causal inference in epidemiology, medicine, and social science.	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).