方法对比

	非线性向量误差修正模型（非线性VECM）	ARDL 边界检验（Pesaran 边界检验）	Johansen协整检验与向量误差修正模型
领域	计量经济学	计量经济学	金融学
方法族	Regression model	Regression model	Regression model
起源年份	1989–1998	2001	1991
提出者	Granger & Lee (1989); Enders & Granger (1998)	Pesaran, Shin & Smith	Søren Johansen
类型	Nonlinear time-series model	Cointegration test / Autoregressive distributed lag model	Multivariate cointegration / vector error correction model
开创性文献	Enders, W., & Granger, C. W. J. (1998). Unit-root tests and asymmetric adjustment with an example using the term structure of interest rates. Journal of Business & Economic Statistics, 16(3), 304–311.	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.	Johansen, S. (1991). Estimation and Hypothesis Testing of Cointegration Vectors in Gaussian Vector Autoregressive Models. Econometrica, 59(6), 1551-1580.
别名	nonlinear VECM, NVECM, threshold VECM, asymmetric VECM	Pesaran bounds test, bounds testing approach, ARDL cointegration test, ARDL Sınır Testi (Pesaran Bounds Test)	Johansen test, VECM, vector error correction model, multivariate cointegration
相关	2	4	3
摘要	The Nonlinear VECM extends the standard linear VECM by allowing the speed of adjustment toward long-run equilibrium to differ depending on the sign, magnitude, or regime of deviations from that equilibrium. It captures asymmetric or threshold-driven dynamics in cointegrated time-series systems that a standard VECM would miss.	The ARDL bounds test is an autoregressive distributed lag method that tests for a cointegrating (long-run level) relationship between time series, introduced by Pesaran, Shin and Smith in 2001. Unlike the Johansen procedure, it remains valid whether the variables are I(0), I(1) or a mix of the two, and it is more reliable than Johansen in small samples of roughly 30 to 80 observations.	The Johansen procedure is a multivariate cointegration framework, introduced by Søren Johansen in 1991, that tests for long-run equilibrium relationships among several I(1) time series. It determines how many cointegrating vectors link the series and then builds a Vector Error Correction Model (VECM) to describe the short-run dynamics around that equilibrium.