Salīdzināt metodes
Apskatiet izvēlētās metodes blakus; rindas, kas atšķiras, ir izceltas.
| Neibiešu nejaušo efektu modelis× | Fiksēto efektu paneļa datu modelis× | |
|---|---|---|
| Nozare | Ekonometrija | Ekonometrija |
| Saime | Regression model | Regression model |
| Izcelsmes gads≠ | 1972–1995 | 2014 |
| Autors≠ | Lindley & Smith (1972); extended by Gelman, Rubin and colleagues | Hsiao (textbook treatment); within transformation of panel data |
| Tips≠ | Bayesian hierarchical panel model | Panel data regression |
| Pirmavots≠ | Gelman, A., Carlin, J. B., Stern, H. S., Dunson, D. B., Vehtari, A., & Rubin, D. B. (2013). Bayesian Data Analysis (3rd ed.). CRC Press. ISBN: 978-1439840955 | Hsiao, C. (2014). Analysis of Panel Data (3rd ed.). Cambridge University Press. DOI ↗ |
| Citi nosaukumi | Bayesian hierarchical model, Bayesian mixed effects model, Bayesian multilevel model, BREM | fixed effects model, within estimator, panel fixed-effects regression, Panel Veri — Sabit Etkiler Modeli |
| Saistītās | 5 | 5 |
| Kopsavilkums≠ | The Bayesian random effects model combines panel-data random effects with a Bayesian prior framework, allowing unit-specific effects to be treated as draws from a population distribution whose hyperparameters are estimated from the data. This produces regularised, uncertainty-quantified estimates that borrow strength across units — particularly valuable for short panels, sparse groups, or settings where frequentist variance-component estimation is unstable. | 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). |
| ScholarGateDatu kopa ↗ |
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