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Fourier Kvantil-på-Kvantil Regresjon×Panel kvantil-på-kvantil regresjon×
FagfeltØkonometriØkonometri
FamilieRegression modelRegression model
Opprinnelsesår2015-2020s2015 (QQ); panel applications from ~2018
OpphavspersonExtension combining Sim & Zhou (2015) QQ regression with Fourier flexible-form smoothingSim and Zhou (cross-section QQ); panel extension in applied energy/finance econometrics
TypeNonparametric quantile regression with Fourier smoothingNonparametric quantile regression
Opprinnelig kildeSim, 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 ↗
AliasFourier QQ regression, Fourier-QQR, Fourier quantile regression with quantile regressors, smooth structural-break QQ regressionPanel QQ regression, panel QQ approach, panel quantile-on-quantile approach, PQQ regression
Relaterte66
SammendragFourier 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.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.
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ScholarGateSammenlign metoder: Fourier Quantile-on-Quantile Regression · Panel Quantile-on-Quantile Regression. Hentet 2026-06-18 fra https://scholargate.app/no/compare