Сравнение методов

	Коррелированные случайные эффекты Мундлака-Чемберлена	Тест спецификации Хаусмана (FE vs RE)	Модель с фиксированными эффектами для панельных данных
Область	Эконометрика	Эконометрика	Эконометрика
Семейство	Regression model	Regression model	Regression model
Год появления	1978	1978	2014
Автор метода	Yair Mundlak; Gary Chamberlain	Jerry A. Hausman	Hsiao (textbook treatment); within transformation of panel data
Тип	Panel data estimator	Specification test for panel data models	Panel data regression
Основополагающий источник	Mundlak, Y. (1978). On the pooling of time series and cross section data. Econometrica, 46(1), 69–85.	Hausman, J. A. (1978). Specification Tests in Econometrics. Econometrica, 46(6), 1251–1271.	Hsiao, C. (2014). Analysis of Panel Data (3rd ed.). Cambridge University Press.
Другие названия	Correlated Random Effects, CRE Estimator, Mundlak Device, Korelasyonlu Rassal Etkiler	Hausman specification test, FE vs RE test, Durbin-Wu-Hausman test, Hausman Spesifikasyon Testi (FE vs RE)	fixed effects model, within estimator, panel fixed-effects regression, Panel Veri — Sabit Etkiler Modeli
Связанные	2	5	5
Сводка	The Mundlak-Chamberlain correlated random effects (CRE) estimator, introduced by Mundlak (1978) and extended by Chamberlain (1982), is a panel data technique that reconciles the fixed effects and random effects approaches by explicitly modelling the correlation between unobserved individual heterogeneity and the observed regressors. By including within-group means of time-varying covariates as additional regressors in a random effects framework, CRE yields estimates numerically equivalent to the within (fixed effects) estimator while permitting identification of time-invariant variables.	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.	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).