השוואת שיטות

	מודל הקמן לבחירת מדגם (Heckit / Tobit Type II)	רגרסיה לוגיסטית	מודל האפקטים הקבועים לנתוני פאנל
תחום	אקונומטריקה	סטטיסטיקה למחקר	אקונומטריקה
משפחה	Regression model	Process / pipeline	Regression model
שנת המקור	1979	1958	2014
הוגה השיטה	James J. Heckman	David Roxbee Cox	Hsiao (textbook treatment); within transformation of panel data
סוג	Two-step sample selection model	Method	Panel data regression
מקור מכונן	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.	Hsiao, C. (2014). Analysis of Panel Data (3rd ed.). Cambridge University Press.
כינויים	heckit, tobit type II, sample selection model, Heckman Seçim Modeli (Heckit / Tobit II)	logit model, binomial logistic regression, LR	fixed effects model, within estimator, panel fixed-effects regression, Panel Veri — Sabit Etkiler Modeli
קשורות	4	3	5
תקציר	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.	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).