Сравнение методов

	Пуассоновская регрессия и регрессия с отрицательным биномиальным распределением	Регрессия методом обыкновенных наименьших квадратов (ОНМК)	Модель с фиксированными эффектами для панельных данных	Квантильная регрессия
Область	Эконометрика	Эконометрика	Эконометрика	Эконометрика
Семейство	Regression model	Regression model	Regression model	Regression model
Год появления	1998	2019	2014	1978
Автор метода	Cameron & Trivedi (textbook treatment); Hilbe (negative binomial)	Wooldridge (textbook treatment); classical least squares	Hsiao (textbook treatment); within transformation of panel data	Koenker & Bassett
Тип	Generalized linear model for count data	Linear regression	Panel data regression	Conditional quantile regression
Основополагающий источник	Cameron, A. C. & Trivedi, P. K. (1998). Regression Analysis of Count Data. Cambridge University Press.	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.
Другие названия	count regression, log-linear count model, negative binomial regression, Poisson / Negatif Binom Regresyon	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
Связанные	4	5	5	5
Сводка	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.	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.