Comparar métodos

	Modelo de Selección Muestral de Heckman (Heckit / Tobit Tipo II)	Regresión por Mínimos Cuadrados Ordinarios (MCO)	Modelo de Efectos Fijos para Datos de Panel
Campo	Econometría	Econometría	Econometría
Familia	Regression model	Regression model	Regression model
Año de origen	1979	2019	2014
Autor original	James J. Heckman	Wooldridge (textbook treatment); classical least squares	Hsiao (textbook treatment); within transformation of panel data
Tipo	Two-step sample selection model	Linear regression	Panel data regression
Fuente seminal	Heckman, J. J. (1979). Sample Selection Bias as a Specification Error. Econometrica, 47(1), 153–161.	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.
Alias	heckit, tobit type II, sample selection model, Heckman Seçim Modeli (Heckit / Tobit II)	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
Relacionados	4	5	5
Resumen	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.	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).