Bandingkan metode

	Uji Spesifikasi Hausman (FE vs RE)	Regresi Kuadrat Terkecil Biasa (Ordinary Least Squares - OLS)	Model Efek Tetap Data Panel
Bidang	Ekonometrika	Ekonometrika	Ekonometrika
Keluarga	Regression model	Regression model	Regression model
Tahun asal	1978	2019	2014
Pencetus	Jerry A. Hausman	Wooldridge (textbook treatment); classical least squares	Hsiao (textbook treatment); within transformation of panel data
Tipe	Specification test for panel data models	Linear regression	Panel data regression
Sumber perintis	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.
Alias	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
Terkait	5	5	5
Ringkasan	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).