Сравнение методов

	Нелинейный анализ панельных данных	Пуассоновская регрессия и регрессия с отрицательным биномиальным распределением
Область	Эконометрика	Эконометрика
Семейство	Regression model	Regression model
Год появления	1986–2010	1998
Автор метода	Cheng Hsiao; Jeffrey M. Wooldridge	Cameron & Trivedi (textbook treatment); Hilbe (negative binomial)
Тип	Panel data model (nonlinear)	Generalized linear model for count data
Основополагающий источник	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.
Другие названия	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
Связанные	4	4
Сводка	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.