Salīdzināt metodes
Apskatiet izvēlētās metodes blakus; rindas, kas atšķiras, ir izceltas.
| Paneļu vienkāršā lineārā regresija× | Jaukto efektu modelis× | |
|---|---|---|
| Nozare | Statistika | Statistika |
| Saime | Regression model | Regression model |
| Izcelsmes gads≠ | 1986 | 1982 |
| Autors≠ | Hsiao (1986); Baltagi (seminal textbook treatments) | Laird & Ware |
| Tips≠ | Linear regression (panel data) | Mixed effects regression |
| Pirmavots≠ | Wooldridge, J. M. (2010). Econometric Analysis of Cross Section and Panel Data (2nd ed.). MIT Press. ISBN: 978-0262232586 | Laird, N. M., & Ware, J. H. (1982). Random-effects models for longitudinal data. Biometrics, 38(4), 963–974. DOI ↗ |
| Citi nosaukumi | panel SLR, longitudinal simple regression, two-way panel simple regression, fixed-effects simple linear regression | LME, LMM, mixed model, random effects model |
| Saistītās≠ | 5 | 4 |
| Kopsavilkums≠ | Panel simple linear regression models a continuous outcome as a linear function of a single predictor using data that track the same entities (individuals, firms, countries) across multiple time periods. It separates within-entity variation from between-entity variation, enabling control for unobserved time-invariant characteristics that would confound a plain cross-sectional regression. | A mixed effects model (or linear mixed model) extends ordinary regression by including both fixed effects — population-level parameters shared by all observations — and random effects that capture subject-, group-, or cluster-level variability. It is the standard tool for repeated-measures, longitudinal, and multilevel data where observations within the same unit are correlated. |
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