So sánh phương pháp

	Kiểm định Đồng tích hợp Hatemi-J với Hai Thay đổi Chế độ	Kiểm định nghiệm đơn vị Lee-Strazicich LM với hai điểm đứt gãy cấu trúc
Lĩnh vực	Kinh tế lượng	Kinh tế lượng
Họ	Hypothesis test	Hypothesis test
Năm ra đời	2008	2003
Người khởi xướng	Abdulnasser Hatemi-J	Junsoo Lee & Mark Strazicich
Loại	Residual-based cointegration test with two structural breaks	Lagrange Multiplier unit-root test with two endogenous structural breaks
Công trình gốc	Hatemi-J, A. (2008). Tests for cointegration with two unknown regime shifts with an application to financial market integration. Empirical Economics, 35(3), 497–505.	Lee, J., & Strazicich, M. C. (2003). Minimum Lagrange multiplier unit root test with two structural breaks. Review of Economics and Statistics, 85(4), 1082–1089.
Tên gọi khác	Hatemi-J Test, Two-Break Cointegration Test, Cointegration Test with Two Regime Shifts, Hatemi-J İki Kırılmalı Eşbütünleşme Testi	LS Unit Root Test, Minimum LM Unit Root Test, Lee-Strazicich Two-Break Test, Lee-Strazicich LM Testi
Liên quan	3	3
Tóm tắt	The Hatemi-J cointegration test, introduced by Abdulnasser Hatemi-J in 2008, tests for a long-run equilibrium relationship between integrated time series while allowing for up to two unknown structural breaks in the cointegrating vector. It extends earlier single-break approaches by permitting both the intercept and slope coefficients of the cointegrating regression to shift at two endogenously determined breakpoints, making it particularly suited for economic and financial data spanning periods of major institutional or policy change.	The Lee-Strazicich (2003) test is a Lagrange Multiplier-based unit-root test that allows for two endogenous structural breaks under both the null and alternative hypotheses. Proposed by Junsoo Lee and Mark C. Strazicich, it corrects a fundamental flaw in earlier break-based tests such as Zivot-Andrews, where structural breaks were permitted only under the alternative. By incorporating breaks under the null, the LS test avoids spurious rejections and provides size-correct inference in the presence of level or trend shifts.