Jämför metoder

	Heckman-modellen för urvalsselektion (Heckit / Tobit typ II)	Logistisk regression	Vanligaste minsta kvadratmetoden (OLS) Regression
Ämnesområde	Ekonometri	Forskningsstatistik	Ekonometri
Familj	Regression model	Process / pipeline	Regression model
Ursprungsår	1979	1958	2019
Upphovsperson	James J. Heckman	David Roxbee Cox	Wooldridge (textbook treatment); classical least squares
Typ	Two-step sample selection model	Method	Linear regression
Ursprungskälla	Heckman, J. J. (1979). Sample Selection Bias as a Specification Error. Econometrica, 47(1), 153–161.	Cox, D. R. (1958). The regression analysis of binary sequences. Journal of the Royal Statistical Society, Series B, 20(2), 215–242.	Wooldridge, J. M. (2019). Introductory Econometrics: A Modern Approach (7th ed.). Cengage Learning. ISBN: 978-1337558860
Alias	heckit, tobit type II, sample selection model, Heckman Seçim Modeli (Heckit / Tobit II)	logit model, binomial logistic regression, LR	ordinary least squares, classical linear regression, linear regression, en küçük kareler regresyonu
Närliggande	4	3	5
Sammanfattning	The Heckman selection model, introduced by James J. Heckman in 1979, is a two-step model that corrects sample selection bias when the outcome is only observed for a non-random subset of cases. A probit selection equation models who is observed, and the outcome equation then corrects for the resulting bias using the inverse Mills ratio.	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.	Ordinary Least Squares is the classical linear regression method that explains a continuous outcome as a linear combination of predictors. It estimates the coefficients by minimising the sum of squared residuals, and under the Gauss-Markov assumptions these estimates are the best linear unbiased estimator (BLUE).