方法对比

	面板KSS	横截面ARDL	Maki 协整检验
领域	计量经济学	计量经济学	计量经济学
方法族	Regression model	Regression model	Regression model
起源年份	1992	2006	2012
提出者	Kwiatkowski, Phillips, Schmidt, and Shin (panel version by Hadri)	Pesaran and colleagues	Darshana Maki
类型	Unit-root test	Dynamic panel model	Structural-break test
开创性文献	Kwiatkowski, D., Phillips, P. C., Schmidt, P., & Shin, Y. (1992). Testing the null hypothesis of stationarity against the alternative of a unit root. Journal of Econometrics, 54(1-3), 159-178.	Pesaran, M. H., & Smith, R. (2016). Testing weak cross-sectional dependence in large panels. Econometric Reviews, 34(6-10), 1089-1117.	Maki, D. (2012). Tests for cointegration allowing for an unknown number of breaks. Economic Modelling, 29(5), 2011-2015.
别名	Panel stationarity test	Panel ARDL with cross-sectional dependence	Structural-break cointegration test
相关	3	3	3
摘要	The Panel KSS test reverses the null hypothesis of unit-root tests: it tests whether variables are stationary (stationarity is the null) versus nonstationary (unit root is the alternative). Introduced by Kwiatkowski et al. (1992) and extended to panels by Hadri (2000), this complementary approach provides robustness when combined with unit-root tests like Panel DF-GLS. Using both tests together reduces the risk of erroneous conclusions about variable persistence.	CS-ARDL (Cross-Sectional ARDL) applies the ARDL framework to panel data while explicitly accounting for cross-sectional dependence—correlation of shocks and relationships across units (countries, firms, regions). Introduced by Pesaran and colleagues (2016), it extends panel ARDL methods to handle common factors or global shocks affecting all units simultaneously. This is crucial for realistic modeling of internationally integrated economies and firm networks.	The Maki cointegration test extends cointegration testing to allow for an unknown number of endogenously-determined structural breaks in the cointegrating relationship. Introduced by Maki (2012), it builds on Gregory and Hansen (1996), enabling detection of cointegration even when relationships shift due to policy changes, institutional reforms, or fundamental regime shifts. This is essential for applied time-series work where structural change is common.