So sánh phương pháp

	Mô hình Hiệu ứng Ngẫu nhiên cho Dữ liệu Bảng	Kiểm định Đặc tả Hausman (FE so với RE)	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	Kinh tế lượng
Họ	Regression model	Regression model	Regression model	Regression model
Năm ra đời	1978	1978	2019	2014
Người khởi xướng	Baltagi (textbook treatment); Hausman specification test	Jerry A. Hausman	Wooldridge (textbook treatment); classical least squares	Hsiao (textbook treatment); within transformation of panel data
Loại	Panel data regression	Specification test for panel data models	Linear regression	Panel data regression
Công trình gốc	Hausman, J. A. (1978). Specification Tests in Econometrics. Econometrica, 46(6), 1251-1271.	Hausman, J. A. (1978). Specification Tests in Econometrics. Econometrica, 46(6), 1251–1271.	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.
Tên gọi khác	random effects panel regression, RE estimator, GLS panel estimator, Panel Rassal Etkiler Modeli	Hausman specification test, FE vs RE test, Durbin-Wu-Hausman test, Hausman Spesifikasyon Testi (FE vs RE)	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	5	5	5	5
Tóm tắt	The random effects model is a panel data estimator that explains an outcome using both within-unit and between-unit variation, treating the unobserved unit-specific heterogeneity as a random, normally distributed term rather than a fixed parameter. Its validity is judged with the Hausman (1978) specification test, and it is developed in standard treatments such as Baltagi's Econometric Analysis of Panel Data.	The Hausman test is a specification test, introduced by Jerry A. Hausman in 1978, that decides between the fixed-effects (FE) and random-effects (RE) estimators in panel data models. The null hypothesis is that the random-effects estimator is consistent and efficient and should be preferred; the alternative is that random effects is inconsistent and fixed effects is required because the unit-specific effects are correlated with the explanatory variables.	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).