Salīdzināt metodes
Apskatiet izvēlētās metodes blakus; rindas, kas atšķiras, ir izceltas.
| Paneļa autoregresijas (Panel AR) modelis× | Modelis ar fiksētajiem efektiem× | |
|---|---|---|
| Nozare | Ekonometrija | Ekonometrija |
| Saime | Regression model | Regression model |
| Izcelsmes gads≠ | 1980s-2000s | 1971–1978 |
| Autors≠ | Hsiao, C.; Arellano, M. | Mundlak (1978); Nerlove (1971); classical panel econometrics |
| Tips≠ | Autoregressive time-series model for panel data | Panel regression estimator |
| Pirmavots≠ | Hsiao, C. (2003). Analysis of Panel Data (2nd ed.). Cambridge University Press. ISBN: 978-0521522717 | Baltagi, B. H. (2021). Econometric Analysis of Panel Data (6th ed.). Springer. ISBN: 978-3030538002 |
| Citi nosaukumi | panel autoregressive model, PAR model, AR model for panel data, panel AR(p) | FE model, within estimator, least squares dummy variable, LSDV regression |
| Saistītās | 5 | 5 |
| Kopsavilkums≠ | The Panel AR model extends the classical univariate autoregressive model to panel data, capturing how each unit's own past values predict its current value while controlling for unobserved individual heterogeneity through fixed or random effects. It is foundational for modelling dynamic persistence in micro or macro panel datasets. | The fixed effects (FE) model is the workhorse estimator for panel data when unobserved unit-specific characteristics are suspected to correlate with the regressors. By absorbing each entity's time-invariant heterogeneity into a separate intercept, FE isolates the causal effect of within-unit variation and eliminates omitted-variable bias from time-constant confounders. |
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