Salīdzināt metodes
Apskatiet izvēlētās metodes blakus; rindas, kas atšķiras, ir izceltas.
| Panel OLS (apvienotie parastie mazākie kvadrāti)× | Modelis ar fiksētajiem efektiem× | |
|---|---|---|
| Nozare | Ekonometrija | Ekonometrija |
| Saime | Regression model | Regression model |
| Izcelsmes gads≠ | 1986-2003 | 1971–1978 |
| Autors≠ | Classical least squares applied to pooled panels; foundational treatment in Hsiao (2003) and Wooldridge (2010) | Mundlak (1978); Nerlove (1971); classical panel econometrics |
| Tips≠ | Linear panel regression | Panel regression estimator |
| 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. ISBN: 978-3030538002 |
| Citi nosaukumi | pooled OLS, pooled ordinary least squares, panel least squares, POLS | FE model, within estimator, least squares dummy variable, LSDV regression |
| Saistītās≠ | 4 | 5 |
| Kopsavilkums≠ | Panel OLS — also called Pooled OLS — applies the classical ordinary least squares estimator to panel data by stacking all cross-sectional units and time periods into a single sample. It estimates one common set of slope coefficients under the assumption that the intercept and slopes are homogeneous across units and time. | 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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