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| Stochastic Frontier Analysis (SFA)× | Model efektów stałych dla danych panelowych× | Regresja kwantylowa× | |
|---|---|---|---|
| Dziedzina | Ekonometria | Ekonometria | Ekonometria |
| Rodzina | Regression model | Regression model | Regression model |
| Rok powstania≠ | 1977 | 2014 | 1978 |
| Twórca≠ | Aigner, Lovell & Schmidt (1977); Battese & Coelli (1995) for panels | Hsiao (textbook treatment); within transformation of panel data | Koenker & Bassett |
| Typ≠ | Frontier regression model | Panel data regression | Conditional quantile regression |
| Źródło pierwotne≠ | 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. DOI ↗ | Hsiao, C. (2014). Analysis of Panel Data (3rd ed.). Cambridge University Press. DOI ↗ | Koenker, R. & Bassett, G., Jr. (1978). Regression Quantiles. Econometrica, 46(1), 33-50. DOI ↗ |
| Inne nazwy≠ | 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 |
| Pokrewne≠ | 3 | 5 | 5 |
| Podsumowanie≠ | 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. |
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