def setup_class(cls):
cls.rng = RandomState(12345)
data = dataset_loader(macrodata)
cls.cpi = log(data["cpi"])
cls.realgdp = data["realgdp"]
cls.inflation = diff(cls.cpi)
cls.inflation_change = diff(cls.inflation)
def setup_class(cls):
cls.rng = RandomState(12345)
t = 1100
y = np.zeros(t)
e = cls.rng.standard_normal(t)
y[:2] = e[:2]
for i in range(3, t):
y[i] = 1.5 * y[i - 1] - 0.8 * y[i - 2] + 0.2 * y[i - 3] + e[i]
cls.y = y[100:]
cls.x = cls.y.std() * cls.rng.randn(t, 2)
cls.x = cls.x[100:]
cls.z = cls.y + cls.x.sum(1)
cls.cpi = log(dataset_loader(macrodata)["cpi"])
cls.inflation = diff(cls.cpi)
cls.inflation_change = diff(cls.inflation)
with pytest.raises(InfeasibleTestException,
match="The maximum lag you are"):
assert np.isfinite(adf.stat)
def test_kpss_buggy_timeseries1():
x = np.asarray([0])
adf = KPSS(x, lags=0)
# ValueError: cannot convert float NaN to integer
with pytest.raises(InfeasibleTestException,
match="A minimum of 2 observations"):
assert np.isfinite(adf.stat)
kpss_autolag_data = (
(dataset_loader(macrodata)["realgdp"], "c", 9),
(dataset_loader(sunspots)["SUNACTIVITY"], "c", 7),
(dataset_loader(nile)["volume"], "c", 5),
(dataset_loader(randhie)["lncoins"], "ct", 75),
(dataset_loader(modechoice)["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)
def setup_class(cls):
cls.rng = RandomState(12345)
cls.cpi = log(dataset_loader(macrodata)['cpi'])
cls.inflation = diff(cls.cpi)
cv_50 = mackinnoncrit(nobs=50)
cv_inf = mackinnoncrit()
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 = ((dataset_loader(macrodata)['realgdp'], 'c',
9), (dataset_loader(sunspots)['SUNACTIVITY'], 'c',
7), (dataset_loader(nile)['volume'], 'c', 5),
(dataset_loader(randhie)['lncoins'], 'ct',
75), (dataset_loader(modechoice)['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', [
def test_autolag_ols_low_memory_smoke(trend, method):
data = dataset_loader(macrodata)
realgdp = np.log(data["realgdp"])
_autolag_ols_low_memory(realgdp, maxlag=4, trend=trend, method=method)
