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| Kvantil-på-kvantil-regression med strukturelt brud× | Kvantil-på-kvantil (QQ) regression× | |
|---|---|---|
| Fagområde | Økonometri | Økonometri |
| Familie | Regression model | Regression model |
| Oprindelsesår≠ | 2015-2020s | 2015 |
| Ophavsperson≠ | Extension combining Sim & Zhou (2015) QQR framework with Bai-Perron structural break methodology | Sim and Zhou |
| Type≠ | Nonparametric quantile regression with structural breaks | Nonparametric quantile regression |
| Oprindelig kilde≠ | Sim, N., and 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 ↗ |
| Aliasser | SB-QQR, structural-break QQ regression, quantile-on-quantile with structural breaks, QQR with regime shifts | QQ regression, QQ approach, quantile-on-quantile approach, nonparametric quantile regression |
| Relaterede | 6 | 6 |
| Resumé≠ | Structural Break Quantile-on-Quantile Regression (SB-QQR) extends the quantile-on-quantile framework of Sim and Zhou (2015) by allowing regression slopes to differ across regimes separated by structural breaks. It maps how the effect of a predictor's quantile on an outcome's quantile changes not only across the full distributional space but also across distinct historical periods or policy regimes. | 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. |
| ScholarGateDatasæt ↗ |
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