Vertaile menetelmiä

	Satunnaisten vaikutusten paneelimalli	Hausmanin spesifikaatiotesti (FE vs RE)	Hierarkkinen lineaarinen mallinnus (HLM / monitasomallinnus)
Tieteenala	Ekonometria	Ekonometria	Tilastotiede
Menetelmäperhe	Regression model	Regression model	Hypothesis test
Syntyvuosi	1978	1978	1986
Kehittäjä	Baltagi (textbook treatment); Hausman specification test	Jerry A. Hausman	Raudenbush & Bryk (popularized); Goldstein (parallel development)
Tyyppi	Panel data regression	Specification test for panel data models	Parametric nested-data regression
Alkuperäislähde	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.	Raudenbush, S.W. & Bryk, A.S. (2002). Hierarchical Linear Models: Applications and Data Analysis Methods (2nd ed.). Sage. ISBN: 978-0761919049
Rinnakkaisnimet	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)	HLM, MLM, multilevel modeling, multilevel analysis
Liittyvät	5	5	4
Tiivistelmä	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.	Hierarchical Linear Modeling (HLM), also known as Multilevel Modeling (MLM), is a parametric statistical method for analyzing nested or clustered data — for example students within classrooms, patients within hospitals, or employees within organizations. Formalized by Raudenbush and Bryk in their 2002 seminal text (building on work from the mid-1980s), HLM simultaneously estimates individual-level and group-level effects while correctly partitioning variance across levels.