def test_adf_autolag():
# GH#246
d2 = macrodata.load_pandas().data
for k_trend, tr in enumerate(['nc', 'c', 'ct', 'ctt']):
x = np.log(d2['realgdp'].values)
xd = np.diff(x)
# check exog
adf3 = unit_root.adfuller(x, maxlag=None, autolag='aic',
regression=tr, store=True, regresults=True)
st2 = adf3[-1]
assert len(st2.autolag_results) == 15 + 1 # +1 for lagged level
for l, res in sorted(list(st2.autolag_results.items()))[:5]:
lag = l - k_trend
# assert correct design matrices in _autolag
assert_equal(res.model.exog[-10:, k_trend], x[-11:-1])
assert_equal(res.model.exog[-1, k_trend + 1:], xd[-lag:-1][::-1])
# min-ic lag of dfgls in Stata is also 2, or 9 for maic
# with notrend
assert st2.usedlag == 2
# same result with lag fixed at usedlag of autolag
adf2 = unit_root.adfuller(x, maxlag=2, autolag=None, regression=tr)
assert_almost_equal(adf3[:2], adf2[:2], decimal=12)
tr = 'c'
# check maxlag with autolag
adf3 = unit_root.adfuller(x, maxlag=5, autolag='aic',
regression=tr, store=True, regresults=True)
assert len(adf3[-1].autolag_results) == 5 + 1
adf3 = unit_root.adfuller(x, maxlag=0, autolag='aic',
regression=tr, store=True, regresults=True)
assert len(adf3[-1].autolag_results) == 0 + 1
def test_store_str(self):
store = unit_root.adfuller(self.y, regression="nc",
autolag=None, maxlag=1,
store=True)[-1]
assert store.__str__() == 'Augmented Dickey-Fuller Test Results'
def setup_class(cls):
cls.res1 = unit_root.adfuller(cls.y, autolag=None, **cls.kwargs)