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	Test di specificazione di Hausman (FE vs RE)	Modellazione Lineare Gerarchica (HLM / Modellazione Multilivello)	Regression with Ordinary Least Squares (OLS)
Campo	Econometria	Statistica	Econometria
Famiglia	Regression model	Hypothesis test	Regression model
Anno di origine	1978	1986	2019
Ideatore	Jerry A. Hausman	Raudenbush & Bryk (popularized); Goldstein (parallel development)	Wooldridge (textbook treatment); classical least squares
Tipo	Specification test for panel data models	Parametric nested-data regression	Linear regression
Fonte seminale	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	Wooldridge, J. M. (2019). Introductory Econometrics: A Modern Approach (7th ed.). Cengage Learning. ISBN: 978-1337558860
Alias	Hausman specification test, FE vs RE test, Durbin-Wu-Hausman test, Hausman Spesifikasyon Testi (FE vs RE)	HLM, MLM, multilevel modeling, multilevel analysis	ordinary least squares, classical linear regression, linear regression, en küçük kareler regresyonu
Correlati	5	4	5
Sintesi	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.	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).