Salīdzināt metodes
Apskatiet izvēlētās metodes blakus; rindas, kas atšķiras, ir izceltas.
| Nelineārā paneļu datu analīze× | Modelis ar nejaušiem efektiem panelī× | |
|---|---|---|
| Nozare | Ekonometrija | Ekonometrija |
| Saime | Regression model | Regression model |
| Izcelsmes gads≠ | 1986–2010 | 2021 |
| Autors≠ | Cheng Hsiao; Jeffrey M. Wooldridge | Baltagi (textbook treatment); classical random-effects panel estimator |
| Tips≠ | Panel data model (nonlinear) | Panel data regression |
| Pirmavots≠ | Wooldridge, J. M. (2010). Econometric Analysis of Cross Section and Panel Data (2nd ed.). MIT Press. ISBN: 978-0262232586 | Baltagi, B. H. (2021). Econometric Analysis of Panel Data (6th ed.). Springer. DOI ↗ |
| Citi nosaukumi | nonlinear panel models, panel nonlinear econometrics, fixed-effects nonlinear models, random-effects nonlinear models | random effects panel model, RE estimator, GLS random effects, Panel Veri — Rassal Etkiler Modeli |
| Saistītās≠ | 4 | 5 |
| Kopsavilkums≠ | Nonlinear panel data analysis applies nonlinear models — such as probit, logit, Poisson, or Tobit — to repeated observations on the same units over time. It accounts for unit-specific unobserved heterogeneity while capturing non-linear relationships between predictors and the outcome, making it essential when the dependent variable is binary, count-based, censored, or otherwise non-continuous. | The Random Effects model is a panel-data regression that treats unobserved individual heterogeneity as a random component drawn from a common distribution, rather than a separate parameter for each unit. It is a standard estimator in panel econometrics, developed in textbook treatments such as Baltagi's Econometric Analysis of Panel Data (2021). |
| ScholarGateDatu kopa ↗ |
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