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结构断裂ARDL边界检验×非线性自回归分布式滞后 (NARDL) 模型×
领域计量经济学计量经济学
方法族Regression modelRegression model
起源年份2001–2010s2014
提出者Pesaran, Shin & Smith (bounds framework); structural break extensions by Bahmani-Oskooee, Enders & Jones, and othersShin, Yu & Greenwood-Nimmo
类型Cointegration / bounds testNonlinear cointegration model
开创性文献Pesaran, M. H., Shin, Y., & Smith, R. J. (2001). Bounds testing approaches to the analysis of level relationships. Journal of Applied Econometrics, 16(3), 289–326. 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 ↗
别名SB-ARDL bounds test, ARDL bounds test with structural break, Fourier ARDL bounds test, break-augmented bounds testingNARDL, nonlinear bounds test, asymmetric ARDL, asymmetric cointegration model
相关65
摘要The structural break ARDL bounds test extends the Pesaran, Shin and Smith (2001) bounds testing framework to accommodate one or more structural breaks in the long-run relationship between time-series variables. By incorporating break dummies or smooth Fourier terms into the ARDL error-correction equation, it allows researchers to test for cointegration even when the data have experienced shifts in intercept or slope caused by policy changes, crises, or regime switches.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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  1. v1
  2. 2 来源
  3. PUBLISHED

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ScholarGate方法对比: Structural Break ARDL Bounds Test · Nonlinear ARDL. 于 2026-06-18 检索自 https://scholargate.app/zh/compare