def test_x_exceptions():
res = ARX(SP500, lags=1).fit(disp="off")
with pytest.raises(TypeError, match="x is not None but"):
res.forecast(reindex=False, x=SP500)
x = SP500.copy()
x[:] = np.random.standard_normal(SP500.shape)
res = ARX(SP500, lags=1, x=x).fit(disp="off")
with pytest.raises(TypeError, match="x is None but the model"):
res.forecast(reindex=False)
res = ARX(SP500, lags=1, x=x).fit(disp="off")
with pytest.raises(ValueError, match="x must have the same"):
res.forecast(reindex=False, x={})
with pytest.raises(ValueError, match="x must have the same"):
res.forecast(reindex=False, x={"x0": x, "x1": x})
with pytest.raises(KeyError, match="The keys of x must exactly"):
res.forecast(reindex=False, x={"z": x})
with pytest.raises(ValueError,
match="The arrays contained in the dictionary"):
_x = np.asarray(x).reshape((1, x.shape[0], 1))
res.forecast(reindex=False, x={"x0": _x})
x2 = pd.concat([x, x], 1)
x2.columns = ["x0", "x1"]
x2.iloc[:, 1] = np.random.standard_normal(SP500.shape)
res = ARX(SP500, lags=1, x=x2).fit(disp="off")
with pytest.raises(ValueError, match="The shapes of the arrays contained"):
res.forecast(reindex=False,
x={
"x0": x2.iloc[:, 0],
"x1": x2.iloc[10:, 1:]
})
with pytest.raises(ValueError, match="1- and 2-dimensional x values"):
res.forecast(reindex=False, x=x2)
with pytest.raises(ValueError, match="The leading dimension of x"):
_x2 = np.asarray(x2)
_x2 = _x2.reshape((1, -1, 2))
res.forecast(reindex=False, x=_x2)
with pytest.raises(ValueError, match="The number of values passed"):
res.forecast(reindex=False, x=np.empty((2, SP500.shape[0], 3)))
with pytest.raises(ValueError,
match="The shape of x does not satisfy the"):
res.forecast(reindex=False, x=np.empty((2, SP500.shape[0] // 2, 1)))
from arch.univariate import ARCH, GARCH
ar.volatility = GARCH(p=3, o=0, q=3)
res = ar.fit(update_freq=0, disp='off')
p(res.summary())
# In[265]:
from arch.univariate import StudentsT
ar.distribution = StudentsT()
res = ar.fit(update_freq=0, disp='off')
p(res.summary())
# In[266]:
arf = ar.forecast(horizon=forecast_steps,
start=Y.index[-1],
params=res.params,
method='simulation')
# In[267]:
plt.plot(idx, arf.simulations.values[-1].T, color='blue', alpha=0.1)
plt.show()
# In[269]:
plt.style.use('bmh')
fig, ax = plt.subplots()
plt.plot(idx,
arf.simulations.variances[-1, ::].T,
color='blue',
alpha=0.1,