Comparar métodos

	Modelo de Regressão Probit	Método de Variáveis Instrumentais (VI) para Inferência Causal	Modelo de Efeitos Fixos para Dados em Painel
Área	Econometria	Economia da saúde	Econometria
Família	Regression model	Process / pipeline	Regression model
Ano de origem	2018	1990s (modern applications)	2014
Autor original	Greene (textbook treatment); classical discrete-choice modelling	Angrist & Pischke (applied econometrics); rooted in econometric theory	Hsiao (textbook treatment); within transformation of panel data
Tipo	Binary discrete-choice model	Method	Panel data regression
Fonte seminal	Greene, W. H. (2018). Econometric Analysis (8th ed.). Pearson. ISBN: 978-0134461366	Angrist, J. D., & Pischke, J. S. (2009). Mostly Harmless Econometrics: An Empiricist's Companion. Princeton: Princeton University Press.	Hsiao, C. (2014). Analysis of Panel Data (3rd ed.). Cambridge University Press.
Outros nomes	probit regression, normit model, Probit Modeli	IV, two-stage least squares, TSLS, causal estimation	fixed effects model, within estimator, panel fixed-effects regression, Panel Veri — Sabit Etkiler Modeli
Relacionados	5	3	5
Resumo	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).	Instrumental variables (IV) is an econometric method to estimate causal effects when treatment or exposure is not randomly assigned and confounding is severe or unmeasured. IV relies on a third variable (instrument) that influences treatment but does not directly affect the outcome, allowing researchers to isolate the causal effect from the noise of confounding. Developed extensively in econometrics (Angrist & Pischke, 1990s–2000s), IV methods are increasingly used in health economics and health services research to leverage natural experiments and policy changes.	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).