Confronta i metodi

	Regressione quantilica	Modello a Effetti Fissi per Dati Panel	Regressione di Poisson e Binomiale Negativa
Campo	Econometria	Econometria	Econometria
Famiglia	Regression model	Regression model	Regression model
Anno di origine	1978	2014	1998
Ideatore	Koenker & Bassett	Hsiao (textbook treatment); within transformation of panel data	Cameron & Trivedi (textbook treatment); Hilbe (negative binomial)
Tipo	Conditional quantile regression	Panel data regression	Generalized linear model for count data
Fonte seminale	Koenker, R. & Bassett, G., Jr. (1978). Regression Quantiles. Econometrica, 46(1), 33-50.	Hsiao, C. (2014). Analysis of Panel Data (3rd ed.). Cambridge University Press.	Cameron, A. C. & Trivedi, P. K. (1998). Regression Analysis of Count Data. Cambridge University Press.
Alias	conditional quantile regression, regression quantiles, Kantil Regresyon	fixed effects model, within estimator, panel fixed-effects regression, Panel Veri — Sabit Etkiler Modeli	count regression, log-linear count model, negative binomial regression, Poisson / Negatif Binom Regresyon
Correlati	5	5	4
Sintesi	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.	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).	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.