手法を比較

	ハウスマンの仕様検定（固定効果モデル vs. 混合効果モデル）	階層線形モデリング（HLM / マルチレベルモデリング）	パネルデータ固定効果モデル
分野	計量経済学	統計学	計量経済学
系統	Regression model	Hypothesis test	Regression model
提唱年	1978	1986	2014
提唱者	Jerry A. Hausman	Raudenbush & Bryk (popularized); Goldstein (parallel development)	Hsiao (textbook treatment); within transformation of panel data
種類	Specification test for panel data models	Parametric nested-data regression	Panel data regression
原典	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	Hsiao, C. (2014). Analysis of Panel Data (3rd ed.). Cambridge University Press.
別名	Hausman specification test, FE vs RE test, Durbin-Wu-Hausman test, Hausman Spesifikasyon Testi (FE vs RE)	HLM, MLM, multilevel modeling, multilevel analysis	fixed effects model, within estimator, panel fixed-effects regression, Panel Veri — Sabit Etkiler Modeli
関連	5	4	5
概要	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.	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).