Comparer des méthodes

	Analyse de données de panel non linéaires	Régression de Poisson et binomiale négative
Domaine	Économétrie	Économétrie
Famille	Regression model	Regression model
Année d'origine	1986–2010	1998
Auteur d'origine	Cheng Hsiao; Jeffrey M. Wooldridge	Cameron & Trivedi (textbook treatment); Hilbe (negative binomial)
Type	Panel data model (nonlinear)	Generalized linear model for count data
Source fondatrice	Wooldridge, J. M. (2010). Econometric Analysis of Cross Section and Panel Data (2nd ed.). MIT Press. ISBN: 978-0262232586	Cameron, A. C. & Trivedi, P. K. (1998). Regression Analysis of Count Data. Cambridge University Press.
Alias	nonlinear panel models, panel nonlinear econometrics, fixed-effects nonlinear models, random-effects nonlinear models	count regression, log-linear count model, negative binomial regression, Poisson / Negatif Binom Regresyon
Apparentées	4	4
Résumé	Nonlinear panel data analysis applies nonlinear models — such as probit, logit, Poisson, or Tobit — to repeated observations on the same units over time. It accounts for unit-specific unobserved heterogeneity while capturing non-linear relationships between predictors and the outcome, making it essential when the dependent variable is binary, count-based, censored, or otherwise non-continuous.	Poisson regression is a generalized linear model for count outcomes — events tallied as non-negative integers such as hospital admissions, accidents, or article counts. It models the log of the expected count as a linear function of the predictors, and is developed in the standard count-data treatment of Cameron and Trivedi (1998); when the counts are over-dispersed, the closely related negative binomial model (Hilbe, 2011) is preferred.