方法对比

	Heckman样本选择模型（Heckit / Tobit II型）	逻辑回归	普通最小二乘法 (OLS) 回归	面板数据固定效应模型	分位数回归
领域	计量经济学	研究统计学	计量经济学	计量经济学	计量经济学
方法族	Regression model	Process / pipeline	Regression model	Regression model	Regression model
起源年份	1979	1958	2019	2014	1978
提出者	James J. Heckman	David Roxbee Cox	Wooldridge (textbook treatment); classical least squares	Hsiao (textbook treatment); within transformation of panel data	Koenker & Bassett
类型	Two-step sample selection model	Method	Linear regression	Panel data regression	Conditional quantile regression
开创性文献	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	Hsiao, C. (2014). Analysis of Panel Data (3rd ed.). Cambridge University Press.	Koenker, R. & Bassett, G., Jr. (1978). Regression Quantiles. Econometrica, 46(1), 33-50.
别名	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	fixed effects model, within estimator, panel fixed-effects regression, Panel Veri — Sabit Etkiler Modeli	conditional quantile regression, regression quantiles, Kantil Regresyon
相关	4	3	5	5	5
摘要	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).	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).	Quantile regression models conditional quantiles of an outcome - the median, the 25th or 75th percentile, and so on - rather than the conditional mean that OLS targets. Introduced by Koenker and Bassett in 1978, it reveals how predictors act across the whole distribution, including its tails.