Python implementation of Zivot-Andrews structural-break unit-root test. Multi-processing version included for large-sample series. Version contained in ZAUnitRoot/ZAUnitRootClass.py has been submitted to statsmodels package.
x : array_like
data series
trim : float
percentage of series at begin/end to exclude from break-period
calculation in range [0, 0.333] (default=0.15)
maxlag : int
maximum lag which is included in test, default=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}
- 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
zastat : float
test statistic
pvalue : float
based on MC-derived critical values
cvdict : dict
critical values for the test statistic at the 1%, 5%, and 10%
levels
bpidx : int
index of x corresponding to endogenously calculated break period
with values in the range [0..nobs-1]
baselag : int
number of lags used for period regressions
Critical values for the three different models are generated through Monte Carlo simulation using 100,000 replications and 2000 data points
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 tradeoff.
Baum, C.F. (2004/2015). 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.
Schwert, G.W. (1989). Tests for unit roots: A Monte Carlo investigation. Journal of Business & Economic Statistics, 7: 147-159.
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.
Python 3.6
Numpy 1.13.1
Statsmodels 0.9.0
Pandas 0.20.3
There are no parameters. The program is set up to access a test file in the ..\results directory. This path can be modified in the source file.
Please see comments in the source file for additional info including referenced output for the test file.