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Model d'efectes aleatoris per a dades de panell×Modelització Lineal Jeràrquica (HLM / Modelització Multinivell)×Model d'efectes fixos per a dades de panell×
CampEconometriaEstadísticaEconometria
FamíliaRegression modelHypothesis testRegression model
Any d'origen197819862014
Autor originalBaltagi (textbook treatment); Hausman specification testRaudenbush & Bryk (popularized); Goldstein (parallel development)Hsiao (textbook treatment); within transformation of panel data
TipusPanel data regressionParametric nested-data regressionPanel data regression
Font seminalHausman, 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: 978-0761919049Hsiao, C. (2014). Analysis of Panel Data (3rd ed.). Cambridge University Press. DOI ↗
Àliesrandom effects panel regression, RE estimator, GLS panel estimator, Panel Rassal Etkiler ModeliHLM, MLM, multilevel modeling, multilevel analysisfixed effects model, within estimator, panel fixed-effects regression, Panel Veri — Sabit Etkiler Modeli
Relacionats545
ResumThe 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.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).
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ScholarGateCompara mètodes: Random Effects Panel Model · Hierarchical Linear Modeling · Panel Fixed Effects. Recuperat el 2026-06-18 de https://scholargate.app/ca/compare