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| Hồi quy Poisson và Âm nhị thức× | Hồi quy Bình phương Tối thiểu Thông thường (OLS)× | Mô hình Hiệu ứng Cố định Dữ liệu Bảng× | |
|---|---|---|---|
| Lĩnh vực | Kinh tế lượng | Kinh tế lượng | Kinh tế lượng |
| Họ | Regression model | Regression model | Regression model |
| Năm ra đời≠ | 1998 | 2019 | 2014 |
| Người khởi xướng≠ | Cameron & Trivedi (textbook treatment); Hilbe (negative binomial) | Wooldridge (textbook treatment); classical least squares | Hsiao (textbook treatment); within transformation of panel data |
| Loại≠ | Generalized linear model for count data | Linear regression | Panel data regression |
| Công trình gốc≠ | Cameron, A. C. & Trivedi, P. K. (1998). Regression Analysis of Count Data. Cambridge University Press. DOI ↗ | 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. DOI ↗ |
| Tên gọi khác | 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 |
| Liên quan≠ | 4 | 5 | 5 |
| Tóm tắt≠ | 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). |
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