Krahasoni metodat
Shqyrtoni metodat e zgjedhura krah për krah; rreshtat që ndryshojnë janë të theksuar.
| Regresioni kuantil-mbi-kuantil panel (Panel Quantile-on-Quantile Regression)× | Regresioni kuantil-mbi-kuantil (QQ)× | |
|---|---|---|
| Fusha | Ekonometri | Ekonometri |
| Familja | Regression model | Regression model |
| Viti i origjinës≠ | 2015 (QQ); panel applications from ~2018 | 2015 |
| Krijuesi≠ | Sim and Zhou (cross-section QQ); panel extension in applied energy/finance econometrics | Sim and Zhou |
| Lloji | Nonparametric quantile regression | Nonparametric quantile regression |
| Burimi themelues | 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 ↗ | 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 ↗ |
| Emërtime të tjera | Panel QQ regression, panel QQ approach, panel quantile-on-quantile approach, PQQ regression | QQ regression, QQ approach, quantile-on-quantile approach, nonparametric quantile regression |
| Të lidhura | 6 | 6 |
| Përmbledhja≠ | 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. | Quantile-on-quantile regression is a nonparametric technique that estimates how the quantiles of one variable depend on the quantiles of another. By combining standard quantile regression with local linear smoothing, it produces a full two-dimensional surface of slope coefficients indexed by both the quantile of the outcome and the quantile of the predictor, revealing heterogeneous and asymmetric dependency structures invisible to standard regression. |
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