Salīdzināt metodes
Apskatiet izvēlētās metodes blakus; rindas, kas atšķiras, ir izceltas.
| Random Effects Panel Model× | Hierarhiskā lineārā modelēšana (HLM / daudzlīmeņu modelēšana)× | Fiksēto efektu paneļa datu modelis× | |
|---|---|---|---|
| Nozare≠ | Ekonometrija | Statistika | Ekonometrija |
| Saime≠ | Regression model | Hypothesis test | Regression model |
| Izcelsmes gads≠ | 1978 | 1986 | 2014 |
| Autors≠ | Baltagi (textbook treatment); Hausman specification test | Raudenbush & Bryk (popularized); Goldstein (parallel development) | Hsiao (textbook treatment); within transformation of panel data |
| Tips≠ | Panel data regression | Parametric nested-data regression | Panel 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 | Hsiao, C. (2014). Analysis of Panel Data (3rd ed.). Cambridge University Press. DOI ↗ |
| Citi nosaukumi≠ | random effects panel regression, RE estimator, GLS panel estimator, Panel Rassal Etkiler Modeli | HLM, MLM, multilevel modeling, multilevel analysis | fixed effects model, within estimator, panel fixed-effects regression, Panel Veri — Sabit Etkiler Modeli |
| Saistītās≠ | 5 | 4 | 5 |
| Kopsavilkums≠ | 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. | 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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