Salīdzināt metodes
Apskatiet izvēlētās metodes blakus; rindas, kas atšķiras, ir izceltas.
| Hausmana specifikācijas tests (FE vs RE)× | Hierarhiskā lineārā modelēšana (HLM / daudzlīmeņu modelēšana)× | |
|---|---|---|
| Nozare≠ | Ekonometrija | Statistika |
| Saime≠ | Regression model | Hypothesis test |
| Izcelsmes gads≠ | 1978 | 1986 |
| Autors≠ | Jerry A. Hausman | Raudenbush & Bryk (popularized); Goldstein (parallel development) |
| Tips≠ | Specification test for panel data models | Parametric nested-data regression |
| Pirmavots≠ | 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 |
| Citi nosaukumi≠ | Hausman specification test, FE vs RE test, Durbin-Wu-Hausman test, Hausman Spesifikasyon Testi (FE vs RE) | HLM, MLM, multilevel modeling, multilevel analysis |
| Saistītās≠ | 5 | 4 |
| Kopsavilkums≠ | 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. |
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