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| Регресия на Поасон и отрицателна биномна регресия× | Модел с фиксирани ефекти за панелни данни× | |
|---|---|---|
| Област | Иконометрия | Иконометрия |
| Семейство | Regression model | Regression model |
| Година на възникване≠ | 1998 | 2014 |
| Създател≠ | Cameron & Trivedi (textbook treatment); Hilbe (negative binomial) | Hsiao (textbook treatment); within transformation of panel data |
| Тип≠ | Generalized linear model for count data | Panel data regression |
| Основополагащ източник≠ | Cameron, A. C. & Trivedi, P. K. (1998). Regression Analysis of Count Data. Cambridge University Press. DOI ↗ | Hsiao, C. (2014). Analysis of Panel Data (3rd ed.). Cambridge University Press. DOI ↗ |
| Други названия | count regression, log-linear count model, negative binomial regression, Poisson / Negatif Binom Regresyon | fixed effects model, within estimator, panel fixed-effects regression, Panel Veri — Sabit Etkiler Modeli |
| Свързани≠ | 4 | 5 |
| Резюме≠ | 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. | 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). |
| ScholarGateНабор от данни ↗ |
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