Compară metode
Examinează metodele selectate una lângă alta; rândurile care diferă sunt evidențiate.
| Model cu Efecte Aleatorii pentru Date Panel× | Modelarea Liniară Ierarhică (HLM / Modelare Multilevel)× | |
|---|---|---|
| Domeniu≠ | Econometrie | Statistică |
| Familie≠ | Regression model | Hypothesis test |
| Anul apariției≠ | 1978 | 1986 |
| Autorul original≠ | Baltagi (textbook treatment); Hausman specification test | Raudenbush & Bryk (popularized); Goldstein (parallel development) |
| Tip≠ | Panel data regression | Parametric nested-data regression |
| Sursa seminală≠ | Hausman, J. A. (1978). Specification Tests in Econometrics. Econometrica, 46(6), 1251-1271. DOI ↗ | Raudenbush, S.W. & Bryk, A.S. (2002). Hierarchical Linear Models: Applications and Data Analysis Methods (2nd ed.). Sage. ISBN: 978-0761919049 |
| Denumiri alternative≠ | random effects panel regression, RE estimator, GLS panel estimator, Panel Rassal Etkiler Modeli | HLM, MLM, multilevel modeling, multilevel analysis |
| Înrudite≠ | 5 | 4 |
| Rezumat≠ | 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. | 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. |
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