Linganisha mbinu

	Kipimo cha Mizizi ya Kitengo cha Zivot-Andrews Kisicho na Mstari	Kipimo cha Mizizi ya Kitengo cha Zivot-Andrews na Mapumziko Moja ya Muundo
Nyanja	Ekonometriki	Ekonometriki
Familia	Regression model	Hypothesis test
Mwaka wa asili	2000s–2010s	1992
Mwanzilishi	Extension combining Zivot & Andrews (1992) with nonlinear STAR-type adjustment; attributed to several applied time-series authors	Eric Zivot & Donald Andrews
Aina	Unit root test with structural break and nonlinear adjustment	Sequential unit-root test with endogenous break-point selection
Chanzo asilia	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.	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.
Majina mbadala	NZA test, nonlinear structural break unit root test, Zivot-Andrews test with nonlinear adjustment, smooth transition Zivot-Andrews test	ZA Test, Zivot-Andrews Break Test, Endogenous Break Unit-Root Test, Zivot-Andrews Birim Kök Testi
Zinazohusiana	2	3
Muhtasari	The Nonlinear Zivot-Andrews test extends the classical Zivot-Andrews structural-break unit root test by embedding smooth-transition nonlinear adjustment into the test regression. It jointly searches for an endogenous structural break and allows the speed of mean-reversion to vary with distance from the attractor, producing more power against nonlinear stationary alternatives than either test alone.	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.