def test_lags(self):
# real GDP from macrodata data set
with warnings.catch_warnings(record=True):
lags = kpss(self.x, 'c', lags='auto')[2]
assert_equal(lags, 9)
# real interest rates from macrodata data set
with warnings.catch_warnings(record=True):
lags = kpss(sunspots.load().data['SUNACTIVITY'], 'c',
lags='auto')[2]
assert_equal(lags, 7)
# volumes from nile data set
with warnings.catch_warnings(record=True):
lags = kpss(nile.load().data['volume'], 'c', lags='auto')[2]
assert_equal(lags, 5)
# log-coinsurance from randhie data set
with warnings.catch_warnings(record=True):
lags = kpss(randhie.load().data['lncoins'], 'ct', lags='auto')[2]
assert_equal(lags, 75)
# in-vehicle time from modechoice data set
with warnings.catch_warnings(record=True):
lags = kpss(modechoice.load().data['invt'], 'ct', lags='auto')[2]
assert_equal(lags, 18)
def test_adf_short_timeseries():
# GH 262
import numpy as np
from arch.unitroot import ADF
x = np.asarray([0., 0., 0., 0., 0., 0., 1., 1., 0., 0.])
adf = ADF(x)
assert_almost_equal(adf.stat, -2.236, decimal=3)
assert adf.lags == 1
kpss_autolag_data = ((macrodata.load().data['realgdp'], 'c', 9),
(sunspots.load().data['SUNACTIVITY'], 'c', 7),
(nile.load().data['volume'], 'c', 5),
(randhie.load().data['lncoins'], 'ct', 75),
(modechoice.load().data['invt'], 'ct', 18))
@pytest.mark.filterwarnings('ignore::DeprecationWarning')
@pytest.mark.parametrize('data,trend,lags', kpss_autolag_data)
def test_kpss_data_dependent_lags(data, trend, lags):
# real GDP from macrodata data set
kpss = KPSS(data, trend=trend)
assert_equal(kpss.lags, lags)
za_test_result = namedtuple('za_test_result',
['stat', 'pvalue', 'lags', 'trend',
'max_lags', 'method', 'actual_lags', ])
