def test_bivariate_eg_null(data, trend, method):
if isinstance(data, pd.DataFrame):
y, x = data.y, data.x
else:
x = data[:, 0]
y = data[:, 1]
test_yx = engle_granger(y, x, trend=trend, method=method)
key = (trend, "y")
assert_allclose(test_yx.stat, NULL_TEST_VALUES[key][0], rtol=1e-4)
assert_allclose(test_yx.rho, 1 + NULL_TEST_VALUES[key][2], rtol=1e-4)
assert_allclose(test_yx.pvalue, NULL_TEST_VALUES[key][1], rtol=1e-2)
test_xy = engle_granger(x, y, trend=trend, method=method)
key = (trend, "x")
assert_allclose(test_xy.stat, NULL_TEST_VALUES[key][0], rtol=1e-4)
assert_allclose(test_xy.rho, 1 + NULL_TEST_VALUES[key][2], rtol=1e-4)
assert_allclose(test_xy.pvalue, NULL_TEST_VALUES[key][1], rtol=1e-2)
s = str(test_xy)
assert "Engle-Granger" in s
assert "Statistic:" in s
assert "ID:" not in s
r = test_yx.__repr__()
assert "ID:" in r
assert test_yx.null_hypothesis == "No Cointegration"
assert test_yx.alternative_hypothesis == "Cointegration"
assert 1 in test_yx.critical_values
assert 5 in test_yx.critical_values
assert 10 in test_yx.critical_values
def test_exceptions(data):
if isinstance(data, pd.DataFrame):
y, x = data.y, data.x
else:
y, x = data[:, :2].T
with pytest.raises(ValueError, match="Unknown trend. Must be one of"):
engle_granger(y, x, trend="nc")
def test_trivariate(data, trend, method, lhs):
rhs = ["x", "y", "z"]
if isinstance(data, pd.DataFrame):
rhs.remove(lhs)
dep, exog = data[lhs], data[rhs]
else:
dep_loc = rhs.index(lhs)
exog_locs = [0, 1, 2]
exog_locs.remove(dep_loc)
dep = data[:, dep_loc]
exog = data[:, exog_locs]
test = engle_granger(dep, exog, trend=trend, method=method)
key = (lhs, trend)
assert_allclose(test.stat, TRIVARIATE[key][0], rtol=1e-4)
assert_allclose(test.rho, 1 + TRIVARIATE[key][2], rtol=1e-4)
assert_allclose(test.pvalue, TRIVARIATE[key][1], rtol=2e-2)
assert test.lags == 0
assert isinstance(test.summary(), Summary)
assert isinstance(test._repr_html_(), str)
ci = test.cointegrating_vector
assert isinstance(ci, pd.Series)
if HAS_MATPLOTLIB:
import matplotlib.pyplot as plt
fig = test.plot()
assert isinstance(fig, plt.Figure)
assert isinstance(test.resid, pd.Series)
assert test.resid.shape[0] == dep.shape[0]
def test_bivariate_eg_alternative(data, trend, method):
if isinstance(data, pd.DataFrame):
y, w = data.y, data.w
else:
w = data[:, -1]
y = data[:, 1]
test_yw = engle_granger(y, w, trend=trend, method=method, max_lags=17)
key = (method, trend, "y")
assert_allclose(test_yw.stat, ALT_TEST_VALUES[key][0], rtol=1e-4)
assert_allclose(test_yw.rho, 1 + ALT_TEST_VALUES[key][2], rtol=1e-4)
assert_allclose(test_yw.pvalue, ALT_TEST_VALUES[key][1], atol=1e-3)
assert test_yw.lags == ALT_TEST_VALUES[key][3]
assert test_yw.max_lags == 17
test_wy = engle_granger(w, y, trend=trend, method=method, max_lags=17)
key = (method, trend, "w")
assert_allclose(test_wy.stat, ALT_TEST_VALUES[key][0], rtol=1e-4)
assert_allclose(test_wy.rho, 1 + ALT_TEST_VALUES[key][2], rtol=1e-4)
assert_allclose(test_wy.pvalue, ALT_TEST_VALUES[key][1], atol=1e-3)
assert test_wy.lags == ALT_TEST_VALUES[key][3]
def test_plot(data):
rhs = ["x", "y", "z"]
lhs = "y"
if isinstance(data, pd.DataFrame):
rhs.remove(lhs)
dep, exog = data[lhs], data[rhs]
else:
dep_loc = rhs.index(lhs)
exog_locs = [0, 1, 2]
exog_locs.remove(dep_loc)
dep = data[:, dep_loc]
exog = data[:, exog_locs]
test = engle_granger(dep, exog)
assert isinstance(test.plot(), plt.Figure)
def test_name_ci_vector(data):
if not isinstance(data, pd.DataFrame):
return
eg = engle_granger(data.w, data[["x", "y", "z"]])
ci = eg.cointegrating_vector
assert list(ci.index) == ["w", "x", "y", "z", "const"]