So sánh phương pháp

	Bayesian NARDL: Ước lượng Hồi quy Phân phối Trễ Phi tuyến theo Phương pháp Bayes	Kiểm định Giới hạn ARDL Bayes
Lĩnh vực	Kinh tế lượng	Kinh tế lượng
Họ	Regression model	Regression model
Năm ra đời	2014 (NARDL); Bayesian extension c. 2015–2020	2001 (ARDL); Bayesian extension 2010s
Người khởi xướng	Shin, Yu & Greenwood-Nimmo (NARDL base); Bayesian extension developed in subsequent applied literature	Pesaran, Shin & Smith (ARDL framework, 2001); Bayesian adaptation by subsequent literature
Loại	Nonlinear cointegrating model with Bayesian inference	Cointegration / bounds testing
Công trình gốc	Shin, 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: Econometric Methods and Applications (pp. 281–314). Springer.	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.
Tên gọi khác	Bayesian NARDL, Bayesian nonlinear ARDL, Bayesian asymmetric ARDL, B-NARDL	Bayesian ARDL, Bayesian bounds testing approach, Bayes ARDL cointegration, Bayesian PSS bounds test
Liên quan	6	5
Tóm tắt	Bayesian NARDL combines the Nonlinear Autoregressive Distributed Lag framework of Shin, Yu, and Greenwood-Nimmo (2014) with Bayesian posterior inference. It models asymmetric long-run cointegration — allowing positive and negative shocks to a regressor to have different equilibrium effects — while incorporating prior knowledge and producing full posterior distributions over all parameters, including the asymmetry gap.	The Bayesian ARDL Bounds Test extends the classical Pesaran-Shin-Smith (2001) bounds testing approach to cointegration by embedding it within a Bayesian inferential framework. Instead of relying on frequentist F- and t-statistics with tabulated critical values, the researcher specifies prior distributions on the model parameters and derives posterior evidence of a long-run level relationship between variables that may be integrated of order zero or one.