Compară metode

	Analiza Frontierelor Stocastice (SFA)	Modelul cu Efecte Fixe pentru Date Panou	Regresia cuantilică
Domeniu	Econometrie	Econometrie	Econometrie
Familie	Regression model	Regression model	Regression model
Anul apariției	1977	2014	1978
Autorul original	Aigner, Lovell & Schmidt (1977); Battese & Coelli (1995) for panels	Hsiao (textbook treatment); within transformation of panel data	Koenker & Bassett
Tip	Frontier regression model	Panel data regression	Conditional quantile regression
Sursa seminală	Aigner, D., Lovell, C.A.K. & Schmidt, P. (1977). Formulation and Estimation of Stochastic Frontier Production Function Models. Journal of Econometrics, 6(1), 21–37.	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.
Denumiri alternative	SFA, stochastic frontier model, stochastic production frontier, Stokastik Sınır Analizi (SFA)	fixed effects model, within estimator, panel fixed-effects regression, Panel Veri — Sabit Etkiler Modeli	conditional quantile regression, regression quantiles, Kantil Regresyon
Înrudite	3	5	5
Rezumat	Stochastic Frontier Analysis is a frontier regression model, introduced by Aigner, Lovell and Schmidt in 1977, that estimates a production, cost, or profit function while separating each unit's technical inefficiency from ordinary statistical noise. It splits the error term into a symmetric random component and a one-sided inefficiency component, producing firm- or country-level efficiency scores.	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.