def test_arch_arx(self):
np.random.seed(12345)
x = np.random.randn(500, 3)
y = x.sum(1) + 3 * np.random.randn(500)
am = ARX(y=y, x=x)
am.fit(disp=DISPLAY).summary()
am.volatility = ARCH(p=2)
results = am.fit(update_freq=0, disp='off')
assert isinstance(results.pvalues, pd.Series), True
assert_equal(list(results.pvalues.index),
['Const', 'x0', 'x1', 'x2',
'omega', 'alpha[1]', 'alpha[2]'])
am = ARX(y=y, lags=2, x=x)
am.fit(disp=DISPLAY).summary()
am.volatility = ARCH(p=2)
results = am.fit(update_freq=0, disp='off')
assert isinstance(results.pvalues, pd.Series)
assert_equal(list(results.pvalues.index),
['Const', 'y[1]', 'y[2]', 'x0', 'x1', 'x2',
'omega', 'alpha[1]', 'alpha[2]'])
x = pd.DataFrame(x, columns=['x0', 'x1', 'x2'])
y = pd.Series(y, name='y')
am = ARX(y=y, x=x)
am.fit(disp=DISPLAY).summary()
am.volatility = ARCH(p=2)
results = am.fit(update_freq=0, disp='off')
assert isinstance(results.pvalues, pd.Series)
assert_equal(list(results.pvalues.index),
['Const', 'x0', 'x1', 'x2',
'omega', 'alpha[1]', 'alpha[2]'])
def test_errors(self):
with pytest.raises(ValueError):
ARX(self.y, lags=np.array([[1, 2], [3, 4]]))
x = self.rng.randn(self.y.shape[0] + 1, 1)
with pytest.raises(ValueError):
ARX(self.y, x=x)
with pytest.raises(ValueError):
HARX(self.y, lags=np.eye(3))
with pytest.raises(ValueError):
ARX(self.y, lags=-1)
with pytest.raises(ValueError):
ARX(self.y, x=self.rng.randn(1, 1), lags=-1)
ar = ARX(self.y, lags=1)
with self.assertRaises(ValueError):
d = Normal()
ar.volatility = d
with self.assertRaises(ValueError):
v = GARCH()
ar.distribution = v
x = self.rng.randn(1000, 1)
with pytest.raises(ValueError):
ar.simulate(np.ones(5), 100, x=x)
with pytest.raises(ValueError):
ar.simulate(np.ones(5), 100)
with pytest.raises(ValueError):
ar.simulate(np.ones(3), 100, initial_value=self.rng.randn(10))
with self.assertRaises(ValueError):
ar.volatility = ConstantVariance()
ar.fit(cov_type='unknown')
def test_errors(self):
with pytest.raises(ValueError):
ARX(self.y, lags=np.array([[1, 2], [3, 4]]))
x = randn(self.y.shape[0] + 1, 1)
with pytest.raises(ValueError):
ARX(self.y, x=x)
with pytest.raises(ValueError):
HARX(self.y, lags=np.eye(3))
with pytest.raises(ValueError):
ARX(self.y, lags=-1)
with pytest.raises(ValueError):
ARX(self.y, x=randn(1, 1), lags=-1)
ar = ARX(self.y, lags=1)
with self.assertRaises(ValueError):
d = Normal()
ar.volatility = d
with self.assertRaises(ValueError):
v = GARCH()
ar.distribution = v
x = randn(1000, 1)
with pytest.raises(ValueError):
ar.simulate(np.ones(5), 100, x=x)
with pytest.raises(ValueError):
ar.simulate(np.ones(5), 100)
with pytest.raises(ValueError):
ar.simulate(np.ones(3), 100, initial_value=randn(10))
with self.assertRaises(ValueError):
ar.volatility = ConstantVariance()
ar.fit(cov_type='unknown')
def test_arch_arx(self):
np.random.seed(12345)
x = np.random.randn(500, 3)
y = x.sum(1) + 3 * np.random.randn(500)
am = ARX(y=y, x=x)
am.fit().summary()
am.volatility = ARCH(p=2)
results = am.fit(update_freq=0, disp='off')
assert_true(isinstance(results.pvalues, pd.Series), True)
assert_equal(list(results.pvalues.index),
['Const', 'x0', 'x1', 'x2',
'omega', 'alpha[1]', 'alpha[2]'])
am = ARX(y=y, lags=2, x=x)
am.fit().summary()
am.volatility = ARCH(p=2)
results = am.fit(update_freq=0, disp='off')
assert_true(isinstance(results.pvalues, pd.Series), True)
assert_equal(list(results.pvalues.index),
['Const', 'y[1]', 'y[2]', 'x0', 'x1', 'x2',
'omega', 'alpha[1]', 'alpha[2]'])
x = pd.DataFrame(x, columns=['x0', 'x1', 'x2'])
y = pd.Series(y, name='y')
am = ARX(y=y, x=x)
am.fit().summary()
am.volatility = ARCH(p=2)
results = am.fit(update_freq=0, disp='off')
assert_true(isinstance(results.pvalues, pd.Series), True)
assert_equal(list(results.pvalues.index),
['Const', 'x0', 'x1', 'x2',
'omega', 'alpha[1]', 'alpha[2]'])
def test_arch_arx(self):
self.rng.seed(12345)
x = self.rng.randn(500, 3)
y = x.sum(1) + 3 * self.rng.standard_normal(500)
am = ARX(y=y, x=x)
res = am.fit(disp=DISPLAY)
res.summary()
assert isinstance(res.optimization_result, OptimizeResult)
am.volatility = ARCH(p=2)
results = am.fit(update_freq=0, disp=DISPLAY)
assert isinstance(results.pvalues, pd.Series)
assert_equal(
list(results.pvalues.index),
["Const", "x0", "x1", "x2", "omega", "alpha[1]", "alpha[2]"],
)
am = ARX(y=y, lags=2, x=x)
res = am.fit(disp=DISPLAY)
summ = res.summary().as_text()
res_repr = res.__repr__()
assert str(hex(id(res))) in res_repr
assert summ[:10] == res_repr[:10]
am.volatility = ARCH(p=2)
results = am.fit(update_freq=0, disp=DISPLAY)
assert isinstance(results.pvalues, pd.Series)
assert_equal(
list(results.pvalues.index),
[
"Const",
"y[1]",
"y[2]",
"x0",
"x1",
"x2",
"omega",
"alpha[1]",
"alpha[2]",
],
)
x = pd.DataFrame(x, columns=["x0", "x1", "x2"])
y = pd.Series(y, name="y")
am = ARX(y=y, x=x)
am.fit(disp=DISPLAY).summary()
am.volatility = ARCH(p=2)
results = am.fit(update_freq=0, disp=DISPLAY)
assert isinstance(results.pvalues, pd.Series)
assert_equal(
list(results.pvalues.index),
["Const", "x0", "x1", "x2", "omega", "alpha[1]", "alpha[2]"],
)
def test_no_param_volatility(self):
cm = ConstantMean(self.y)
cm.volatility = EWMAVariance()
cm.fit(update_freq=0)
cm.volatility = RiskMetrics2006()
cm.fit(update_freq=0)
ar = ARX(self.y, lags=5)
ar.volatility = EWMAVariance()
ar.fit(update_freq=0)
ar.volatility = RiskMetrics2006()
ar.fit(update_freq=0)
def test_no_param_volatility(self):
cm = ConstantMean(self.y)
cm.volatility = EWMAVariance()
cm.fit(update_freq=0)
cm.volatility = RiskMetrics2006()
cm.fit(update_freq=0)
ar = ARX(self.y, lags=5)
ar.volatility = EWMAVariance()
ar.fit(update_freq=0)
ar.volatility = RiskMetrics2006()
ar.fit(update_freq=0)
def test_no_param_volatility(self):
cm = ConstantMean(self.y)
cm.volatility = EWMAVariance()
cm.fit(update_freq=0, disp=DISPLAY)
cm.volatility = RiskMetrics2006()
cm.fit(update_freq=0, disp=DISPLAY)
ar = ARX(self.y, lags=5)
ar.volatility = EWMAVariance()
ar.fit(update_freq=0, disp=DISPLAY)
ar.volatility = RiskMetrics2006()
ar.fit(update_freq=0, disp=DISPLAY)
assert 'tau0' in str(ar.volatility)
assert 'tau1' in str(ar.volatility)
assert 'kmax' in str(ar.volatility)
def test_no_param_volatility(self):
cm = ConstantMean(self.y)
cm.volatility = EWMAVariance()
cm.fit(update_freq=0, disp=DISPLAY)
cm.volatility = RiskMetrics2006()
cm.fit(update_freq=0, disp=DISPLAY)
ar = ARX(self.y, lags=5)
ar.volatility = EWMAVariance()
ar.fit(update_freq=0, disp=DISPLAY)
ar.volatility = RiskMetrics2006()
ar.fit(update_freq=0, disp=DISPLAY)
assert 'tau0' in str(ar.volatility)
assert 'tau1' in str(ar.volatility)
assert 'kmax' in str(ar.volatility)
def test_first_last_obs(self):
ar = ARX(self.y, lags=5, hold_back=100)
res = ar.fit(update_freq=0, disp=DISPLAY)
resids = res.resid
resid_copy = resids.copy()
resid_copy[:100] = np.nan
assert_equal(resids, resid_copy)
ar.volatility = GARCH()
res = ar.fit(update_freq=0, disp=DISPLAY)
resids = res.resid
resid_copy = resids.copy()
resid_copy[:100] = np.nan
assert_equal(resids, resid_copy)
ar = ARX(self.y, lags=5)
ar.volatility = GARCH()
res = ar.fit(update_freq=0, last_obs=500, disp=DISPLAY)
resids = res.resid
resid_copy = resids.copy()
resid_copy[500:] = np.nan
assert_equal(resids, resid_copy)
ar = ARX(self.y, lags=5, hold_back=100)
ar.volatility = GARCH()
res = ar.fit(update_freq=0, last_obs=500, disp=DISPLAY)
resids = res.resid
resid_copy = resids.copy()
resid_copy[:100] = np.nan
resid_copy[500:] = np.nan
assert_equal(resids, resid_copy)
vol = res.conditional_volatility
vol_copy = vol.copy()
vol_copy[:100] = np.nan
vol_copy[500:] = np.nan
assert_equal(vol, vol_copy)
assert_equal(self.y.shape[0], vol.shape[0])
ar = ARX(self.y, lags=5)
ar.volatility = GARCH()
res = ar.fit(update_freq=0, last_obs=500, disp=DISPLAY)
resids = res.resid
resid_copy = resids.copy()
resid_copy[:5] = np.nan
resid_copy[500:] = np.nan
assert_equal(resids, resid_copy)
def test_first_last_obs(self):
ar = ARX(self.y, lags=5, hold_back=100)
res = ar.fit(update_freq=0, disp=DISPLAY)
resids = res.resid
resid_copy = resids.copy()
resid_copy[:100] = np.nan
assert_equal(resids, resid_copy)
ar.volatility = GARCH()
res = ar.fit(update_freq=0, disp=DISPLAY)
resids = res.resid
resid_copy = resids.copy()
resid_copy[:100] = np.nan
assert_equal(resids, resid_copy)
ar = ARX(self.y, lags=5)
ar.volatility = GARCH()
res = ar.fit(update_freq=0, last_obs=500, disp=DISPLAY)
resids = res.resid
resid_copy = resids.copy()
resid_copy[500:] = np.nan
assert_equal(resids, resid_copy)
ar = ARX(self.y, lags=5, hold_back=100)
ar.volatility = GARCH()
res = ar.fit(update_freq=0, last_obs=500, disp=DISPLAY)
resids = res.resid
resid_copy = resids.copy()
resid_copy[:100] = np.nan
resid_copy[500:] = np.nan
assert_equal(resids, resid_copy)
vol = res.conditional_volatility
vol_copy = vol.copy()
vol_copy[:100] = np.nan
vol_copy[500:] = np.nan
assert_equal(vol, vol_copy)
assert_equal(self.y.shape[0], vol.shape[0])
ar = ARX(self.y, lags=5)
ar.volatility = GARCH()
res = ar.fit(update_freq=0, last_obs=500, disp=DISPLAY)
resids = res.resid
resid_copy = resids.copy()
resid_copy[:5] = np.nan
resid_copy[500:] = np.nan
assert_equal(resids, resid_copy)
def test_arch_arx(self):
self.rng.seed(12345)
x = self.rng.randn(500, 3)
y = x.sum(1) + 3 * self.rng.randn(500)
am = ARX(y=y, x=x)
res = am.fit(disp=DISPLAY)
res.summary()
assert isinstance(res.optimization_result, OptimizeResult)
am.volatility = ARCH(p=2)
results = am.fit(update_freq=0, disp=DISPLAY)
assert isinstance(results.pvalues, pd.Series)
assert_equal(
list(results.pvalues.index),
['Const', 'x0', 'x1', 'x2', 'omega', 'alpha[1]', 'alpha[2]'])
am = ARX(y=y, lags=2, x=x)
res = am.fit(disp=DISPLAY)
summ = res.summary().as_text()
repr = res.__repr__()
assert str(hex(id(res))) in repr
assert summ[:10] == repr[:10]
am.volatility = ARCH(p=2)
results = am.fit(update_freq=0, disp=DISPLAY)
assert isinstance(results.pvalues, pd.Series)
assert_equal(list(results.pvalues.index), [
'Const', 'y[1]', 'y[2]', 'x0', 'x1', 'x2', 'omega', 'alpha[1]',
'alpha[2]'
])
x = pd.DataFrame(x, columns=['x0', 'x1', 'x2'])
y = pd.Series(y, name='y')
am = ARX(y=y, x=x)
am.fit(disp=DISPLAY).summary()
am.volatility = ARCH(p=2)
results = am.fit(update_freq=0, disp=DISPLAY)
assert isinstance(results.pvalues, pd.Series)
assert_equal(
list(results.pvalues.index),
['Const', 'x0', 'x1', 'x2', 'omega', 'alpha[1]', 'alpha[2]'])
def test_arch_arx(self):
np.random.seed(12345)
x = np.random.randn(500, 3)
y = x.sum(1) + 3 * np.random.randn(500)
am = ARX(y=y, x=x)
am.fit(disp=DISPLAY).summary()
am.volatility = ARCH(p=2)
results = am.fit(update_freq=0, disp='off')
assert isinstance(results.pvalues, pd.Series), True
assert_equal(list(results.pvalues.index),
['Const', 'x0', 'x1', 'x2',
'omega', 'alpha[1]', 'alpha[2]'])
am = ARX(y=y, lags=2, x=x)
res = am.fit(disp=DISPLAY)
summ = res.summary().as_text()
repr = res.__repr__()
assert str(hex(id(res))) in repr
assert summ[:10] == repr[:10]
am.volatility = ARCH(p=2)
results = am.fit(update_freq=0, disp='off')
assert isinstance(results.pvalues, pd.Series)
assert_equal(list(results.pvalues.index),
['Const', 'y[1]', 'y[2]', 'x0', 'x1', 'x2',
'omega', 'alpha[1]', 'alpha[2]'])
x = pd.DataFrame(x, columns=['x0', 'x1', 'x2'])
y = pd.Series(y, name='y')
am = ARX(y=y, x=x)
am.fit(disp=DISPLAY).summary()
am.volatility = ARCH(p=2)
results = am.fit(update_freq=0, disp='off')
assert isinstance(results.pvalues, pd.Series)
assert_equal(list(results.pvalues.index),
['Const', 'x0', 'x1', 'x2',
'omega', 'alpha[1]', 'alpha[2]'])
