statsmodels.tsa.stattools.zivot_andrews¶
- statsmodels.tsa.stattools.zivot_andrews = <statsmodels.tsa.stattools.ZivotAndrewsUnitRoot object>¶
Zivot-Andrews structural-break unit-root test.
The Zivot-Andrews test tests for a unit root in a univariate process in the presence of serial correlation and a single structural break.
- Parameters:
- xarray_like
The data series to test.
- trim
float The percentage of series at begin/end to exclude from break-period calculation in range [0, 0.333] (default=0.15).
- maxlag
int The maximum lag which is included in test, default is 12*(nobs/100)^{1/4} (Schwert, 1989).
- regression{“c”,”t”,”ct”}
Constant and trend order to include in regression.
“c” : constant only (default).
“t” : trend only.
“ct” : constant and trend.
- autolag{“AIC”, “BIC”, “t-stat”,
None} The method to select the lag length when using automatic selection.
if None, then maxlag lags are used,
if “AIC” (default) or “BIC”, then the number of lags is chosen to minimize the corresponding information criterion,
“t-stat” based choice of maxlag. Starts with maxlag and drops a lag until the t-statistic on the last lag length is significant using a 5%-sized test.
- Returns:
- zastat
float The test statistic.
- pvalue
float The pvalue based on MC-derived critical values.
- cvdict
dict The critical values for the test statistic at the 1%, 5%, and 10% levels.
- baselag
int The number of lags used for period regressions.
- bpidx
int The index of x corresponding to endogenously calculated break period with values in the range [0..nobs-1].
- zastat
Notes
H0 = unit root with a single structural break
Algorithm follows Baum (2004/2015) approximation to original Zivot-Andrews method. Rather than performing an autolag regression at each candidate break period (as per the original paper), a single autolag regression is run up-front on the base model (constant + trend with no dummies) to determine the best lag length. This lag length is then used for all subsequent break-period regressions. This results in significant run time reduction but also slightly more pessimistic test statistics than the original Zivot-Andrews method, although no attempt has been made to characterize the size/power trade-off.
References
[1]Baum, C.F. (2004). ZANDREWS: Stata module to calculate Zivot-Andrews unit root test in presence of structural break,” Statistical Software Components S437301, Boston College Department of Economics, revised 2015.
[2]Schwert, G.W. (1989). Tests for unit roots: A Monte Carlo investigation. Journal of Business & Economic Statistics, 7: 147-159.
[3]Zivot, E., and Andrews, D.W.K. (1992). Further evidence on the great crash, the oil-price shock, and the unit-root hypothesis. Journal of Business & Economic Studies, 10: 251-270.