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फूरियर क्वांटाइल-ऑन-क्वांटाइल रिग्रेशन×गैर-रेखीय एआरडीएल (NARDL) मॉडल×
क्षेत्रअर्थमितिअर्थमिति
परिवारRegression modelRegression model
उद्भव वर्ष2015-2020s2014
प्रवर्तकExtension combining Sim & Zhou (2015) QQ regression with Fourier flexible-form smoothingShin, Yu & Greenwood-Nimmo
प्रकारNonparametric quantile regression with Fourier smoothingNonlinear cointegration model
मौलिक स्रोतSim, N., & Zhou, H. (2015). Oil prices, US stock return, and the dependence between their quantiles. Journal of Banking and Finance, 55, 1-8. DOI ↗Shin, Y., Yu, B., & Greenwood-Nimmo, M. (2014). Modelling asymmetric cointegration and dynamic multipliers in a nonlinear ARDL framework. In R. C. Sickles & W. C. Horrace (Eds.), Festschrift in Honor of Peter Schmidt: Econometric Methods and Applications (pp. 281–314). Springer. link ↗
उपनामFourier QQ regression, Fourier-QQR, Fourier quantile regression with quantile regressors, smooth structural-break QQ regressionNARDL, nonlinear bounds test, asymmetric ARDL, asymmetric cointegration model
संबंधित65
सारांशFourier 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 Nonlinear ARDL (NARDL) model extends the linear ARDL bounds-testing framework to allow asymmetric long-run and short-run relationships. By decomposing the regressor into cumulative positive and negative partial sums, it tests whether increases and decreases in a variable exert different effects on the outcome — a feature especially relevant in financial and energy economics where positive and negative shocks rarely cancel out symmetrically.
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  3. PUBLISHED

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ScholarGateविधियों की तुलना करें: Fourier Quantile-on-Quantile Regression · Nonlinear ARDL. 2026-06-19 को यहाँ से प्राप्त https://scholargate.app/hi/compare