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| Fourier Kvantil-på-Kvantil Regression× | Kvantil-på-kvantil (QQ) regression× | |
|---|---|---|
| Fagområde | Økonometri | Økonometri |
| Familie | Regression model | Regression model |
| Oprindelsesår≠ | 2015-2020s | 2015 |
| Ophavsperson≠ | Extension combining Sim & Zhou (2015) QQ regression with Fourier flexible-form smoothing | Sim and Zhou |
| Type≠ | Nonparametric quantile regression with Fourier smoothing | Nonparametric quantile regression |
| Oprindelig kilde | 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 ↗ |
| Aliasser | Fourier QQ regression, Fourier-QQR, Fourier quantile regression with quantile regressors, smooth structural-break QQ regression | QQ regression, QQ approach, quantile-on-quantile approach, nonparametric quantile regression |
| Relaterede | 6 | 6 |
| Resumé≠ | Fourier quantile-on-quantile regression extends the quantile-on-quantile (QQ) framework of Sim and Zhou (2015) by embedding Fourier trigonometric terms into the local linear quantile model. This allows the estimated dependence between the quantiles of one variable and the quantiles of another to vary smoothly over time, capturing gradual structural change without imposing a known break date. | 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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