def main():
"""
Toy program to test the KPSS autolag method of Hobijn et al (1998).
Unit tests using statsmodels data sets verified against SAS 9.3. To
use, modify the following lines in main KPSS method:
old:
if lags is None:
# from Kwiatkowski et al. referencing Schwert (1989)
lags = int(np.ceil(12. * np.power(nobs / 100., 1 / 4.)))
new:
if lags is None:
# autolag method of Hobijn et al. (1998)
lags = _kpss_autolag(resids, nobs)
"""
print("KPSS autolag method of Hobijn et al. (1998)")
# real GDP from macrodata data set
with warnings.catch_warnings(record=True) as w:
res = kpss(macrodata.load().data['realgdp'], 'c')
print(" realgdp('c'): stat =", "{0:0.5f}".format(res[0]), " pval =",
"{0:0.5f}".format(res[1]), " lags =", format(res[2]))
assert_almost_equal(res[0], 2.06851, decimal=3)
assert_equal(res[2], 9)
# sunspot activity from sunspots data set
with warnings.catch_warnings(record=True) as w:
res = kpss(sunspots.load().data['SUNACTIVITY'], 'c')
print(" sunactivity('c'): stat =", "{0:0.5f}".format(res[0]), " pval =",
"{0:0.5f}".format(res[1]), " lags =", format(res[2]))
assert_almost_equal(res[0], 0.66987, decimal=3)
assert_equal(res[2], 7)
# volumes from nile data set
with warnings.catch_warnings(record=True) as w:
res = kpss(nile.load().data['volume'], 'c')
print(" volume('c'): stat =", "{0:0.5f}".format(res[0]), " pval =",
"{0:0.5f}".format(res[1]), " lags =", format(res[2]))
assert_almost_equal(res[0], 0.86912, decimal=3)
assert_equal(res[2], 5)
# log-coinsurance from randhie data set
with warnings.catch_warnings(record=True) as w:
res = kpss(randhie.load().data['lncoins'], 'ct')
print(" lncoins('ct'): stat =", "{0:0.5f}".format(res[0]), " pval =",
"{0:0.5f}".format(res[1]), " lags =", format(res[2]))
assert_almost_equal(res[0], 0.36762, decimal=3)
assert_equal(res[2], 75)
# in-vehicle time from modechoice data set
with warnings.catch_warnings(record=True) as w:
res = kpss(modechoice.load().data['invt'], 'ct')
print(" invt('ct'): stat =", "{0:0.5f}".format(res[0]), " pval =",
"{0:0.5f}".format(res[1]), " lags =", format(res[2]))
assert_almost_equal(res[0], 0.40258, decimal=3)
assert_equal(res[2], 18)
def test_lags(self):
# real GDP from macrodata data set
with pytest.warns(InterpolationWarning):
res = kpss(self.x, 'c', nlags='auto')
assert_equal(res[2], 9)
# real interest rates from macrodata data set
res = kpss(sunspots.load(True).data['SUNACTIVITY'], 'c', nlags='auto')
assert_equal(res[2], 7)
# volumes from nile data set
with pytest.warns(InterpolationWarning):
res = kpss(nile.load(True).data['volume'], 'c', nlags='auto')
assert_equal(res[2], 5)
# log-coinsurance from randhie data set
with pytest.warns(InterpolationWarning):
res = kpss(randhie.load(True).data['lncoins'], 'ct', nlags='auto')
assert_equal(res[2], 75)
# in-vehicle time from modechoice data set
with pytest.warns(InterpolationWarning):
res = kpss(modechoice.load(True).data['invt'], 'ct', nlags='auto')
assert_equal(res[2], 18)
def test_lags(self):
# real GDP from macrodata data set
with pytest.warns(InterpolationWarning):
res = kpss(self.x, "c", nlags="auto")
assert_equal(res[2], 9)
# real interest rates from macrodata data set
res = kpss(sunspots.load().data["SUNACTIVITY"], "c", nlags="auto")
assert_equal(res[2], 7)
# volumes from nile data set
with pytest.warns(InterpolationWarning):
res = kpss(nile.load().data["volume"], "c", nlags="auto")
assert_equal(res[2], 5)
# log-coinsurance from randhie data set
with pytest.warns(InterpolationWarning):
res = kpss(randhie.load().data["lncoins"], "ct", nlags="auto")
assert_equal(res[2], 75)
# in-vehicle time from modechoice data set
with pytest.warns(InterpolationWarning):
res = kpss(modechoice.load().data["invt"], "ct", nlags="auto")
assert_equal(res[2], 18)
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_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)
assert np.all(cv_50 <= cv_inf)
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',
assert np.all(cv_50 <= cv_inf)
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', ])
