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Structural Break NARDL×Mô hình ARDL phi tuyến (NARDL)×
Lĩnh vựcKinh tế lượngKinh tế lượng
HọRegression modelRegression model
Năm ra đời2014–20182014
Người khởi xướngShin, Yu & Greenwood-Nimmo (NARDL base); structural break extensions by subsequent applied researchersShin, Yu & Greenwood-Nimmo
LoạiNonlinear cointegration with structural breaksNonlinear cointegration model
Công trình gốcShin, Y., Yu, B., & Greenwood-Nimmo, M. (2014). Modelling asymmetric cointegration and dynamic multipliers in a nonlinear ARDL framework. In W. C. Horrace & R. C. Sickles (Eds.), Festschrift in Honor of Peter Schmidt (pp. 281–314). Springer. 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 ↗
Tên gọi khácSB-NARDL, NARDL with structural breaks, nonlinear ARDL with break, asymmetric ARDL structural breakNARDL, nonlinear bounds test, asymmetric ARDL, asymmetric cointegration model
Liên quan65
Tóm tắtStructural Break NARDL extends the Nonlinear Autoregressive Distributed Lag (NARDL) bounds-testing framework by explicitly accommodating one or more structural breaks in the long-run relationship. It separates positive and negative changes in the regressor, tests for cointegration, and allows regime shifts, providing a richer picture of asymmetric and break-sensitive dynamics between variables.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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ScholarGateSo sánh phương pháp: Structural Break NARDL · Nonlinear ARDL. Truy cập ngày 2026-06-17 từ https://scholargate.app/vi/compare