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| Fourier Quantile-on-Quantile Regression× | Fourier-Granger-Kausalitätstest× | |
|---|---|---|
| Fachgebiet | Ökonometrie | Ökonometrie |
| Familie | Regression model | Regression model |
| Entstehungsjahr≠ | 2015-2020s | 2016 |
| Urheber≠ | Extension combining Sim & Zhou (2015) QQ regression with Fourier flexible-form smoothing | Enders and Jones |
| Typ≠ | Nonparametric quantile regression with Fourier smoothing | Causality test |
| Wegweisende Quelle≠ | 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 ↗ | Enders, W., & Jones, P. (2016). Grain prices, oil prices, and multiple smooth breaks in a VAR. Studies in Nonlinear Dynamics and Econometrics, 20(4), 399–419. DOI ↗ |
| Aliasnamen | Fourier QQ regression, Fourier-QQR, Fourier quantile regression with quantile regressors, smooth structural-break QQ regression | Fourier Granger causality test, Enders-Jones Granger causality, smooth structural break Granger test, spectral Granger causality |
| Verwandt | 6 | 6 |
| Zusammenfassung≠ | 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. | The Fourier Granger causality test extends the classic Granger causality framework by embedding low-frequency Fourier terms in the VAR equation, allowing the causal relationship to shift gradually over time without requiring the researcher to pre-specify the number or location of structural breaks. |
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