Σύγκριση μεθόδων

	Δοκιμή Συνολοκλήρωσης Hatemi-J με Δύο Μετατοπίσεις Καθεστώτος	Έλεγχος Μονάδας Ρίζας Lee-Strazicich LM με Δύο Διαρθρωτικές Αλλαγές
Πεδίο	Οικονομετρία	Οικονομετρία
Οικογένεια	Hypothesis test	Hypothesis test
Έτος προέλευσης	2008	2003
Δημιουργός	Abdulnasser Hatemi-J	Junsoo Lee & Mark Strazicich
Τύπος	Residual-based cointegration test with two structural breaks	Lagrange Multiplier unit-root test with two endogenous structural breaks
Θεμελιώδης πηγή	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.
Εναλλακτικές ονομασίες	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
Συναφείς	3	3
Σύνοψη	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.