Vertaile menetelmiä
Tarkastele valitsemiasi menetelmiä rinnakkain; eroavat rivit korostetaan.
| Paneelin kvantiili-kvantiili-regressio× | Paneeli yleistetty pienimmän neliösumman menetelmä (Paneeli GLS)× | |
|---|---|---|
| Tieteenala | Ekonometria | Ekonometria |
| Menetelmäperhe | Regression model | Regression model |
| Syntyvuosi≠ | 2015 (QQ); panel applications from ~2018 | 1935 / developed for panels 1980s–1990s |
| Kehittäjä≠ | Sim and Zhou (cross-section QQ); panel extension in applied energy/finance econometrics | Aitken (1935); extended to panel data by Baltagi and others |
| Tyyppi≠ | Nonparametric quantile regression | Generalized linear regression |
| Alkuperäislähde≠ | 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 |
| Rinnakkaisnimet | Panel QQ regression, panel QQ approach, panel quantile-on-quantile approach, PQQ regression | Panel GLS, Generalized Least Squares for panel data, FGLS panel, feasible GLS panel |
| Liittyvät≠ | 6 | 3 |
| Tiivistelmä≠ | 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. | Panel GLS is a regression method for longitudinal data that explicitly models the non-spherical error structure — heteroscedasticity across units and serial correlation within units — to recover efficient coefficient estimates. Unlike OLS, it weights observations by the inverse of the error covariance matrix, yielding the Best Linear Unbiased Estimator when the error structure is correctly specified. |
| ScholarGateAineisto ↗ |
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