Salīdzināt metodes
Apskatiet izvēlētās metodes blakus; rindas, kas atšķiras, ir izceltas.
| Reģresijas kvantiļu uz kvantiļu panelī× | Fiksēto efektu paneļa modelis (FE)× | |
|---|---|---|
| Nozare | Ekonometrija | Ekonometrija |
| Saime | Regression model | Regression model |
| Izcelsmes gads≠ | 2015 (QQ); panel applications from ~2018 | 1978 |
| Autors≠ | Sim and Zhou (cross-section QQ); panel extension in applied energy/finance econometrics | Mundlak (1978); classical treatment in Wooldridge (2010) and Baltagi (2021) |
| Tips≠ | Nonparametric quantile regression | Panel regression estimator |
| Pirmavots≠ | Sim, N., & Zhou, H. (2015). Oil prices, US stock return, and the dependence between their quantiles. Journal of Banking and Finance, 55, 1-8. DOI ↗ | Wooldridge, J. M. (2010). Econometric Analysis of Cross Section and Panel Data (2nd ed.). MIT Press. ISBN: 978-0262232586 |
| Citi nosaukumi | Panel QQ regression, panel QQ approach, panel quantile-on-quantile approach, PQQ regression | within estimator, FE model, within-group estimator, LSDV model |
| Saistītās≠ | 6 | 5 |
| Kopsavilkums≠ | Panel quantile-on-quantile (QQ) regression jointly maps any quantile of the outcome distribution onto any quantile of the predictor distribution across multiple cross-sectional units observed over time. It generalises Sim and Zhou's (2015) cross-sectional QQ framework to a panel setting, revealing a full dependence surface rather than a single average effect, while accounting for individual heterogeneity through fixed or random effects correction. | The panel fixed effects (FE) model controls for all time-invariant, unit-specific unobserved heterogeneity by absorbing it into individual intercepts. By sweeping out unit means through the within transformation, FE yields unbiased estimates of the effect of time-varying regressors even when omitted unit-level confounders are correlated with those regressors. |
| ScholarGateDatu kopa ↗ |
|
|