Σύγκριση μεθόδων

	Έλεγχος Μοναδιαίας Ρίζας Zivot-Andrews με μία Διαρθρωτική Αλλαγή	Έλεγχος Μονάδας Ρίζας Lee-Strazicich LM με Δύο Διαρθρωτικές Αλλαγές
Πεδίο	Οικονομετρία	Οικονομετρία
Οικογένεια	Hypothesis test	Hypothesis test
Έτος προέλευσης	1992	2003
Δημιουργός	Eric Zivot & Donald Andrews	Junsoo Lee & Mark Strazicich
Τύπος	Sequential unit-root test with endogenous break-point selection	Lagrange Multiplier unit-root test with two endogenous structural breaks
Θεμελιώδης πηγή	Zivot, E., & Andrews, D. W. K. (1992). Further evidence on the great crash, the oil-price shock, and the unit-root hypothesis. Journal of Business & Economic Statistics, 10(3), 251–270.	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	ZA Test, Zivot-Andrews Break Test, Endogenous Break Unit-Root Test, Zivot-Andrews Birim Kök Testi	LS Unit Root Test, Minimum LM Unit Root Test, Lee-Strazicich Two-Break Test, Lee-Strazicich LM Testi
Συναφείς	3	3
Σύνοψη	The Zivot-Andrews (ZA) test, introduced by Eric Zivot and Donald Andrews in 1992, is a sequential unit-root test that allows for a single structural break at an unknown date. It extends the augmented Dickey-Fuller framework by endogenously selecting the break point that provides the strongest evidence against the unit-root null hypothesis, making it particularly useful for macroeconomic and financial time series that may have been disrupted by events such as policy changes, financial crises, or supply shocks.	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.