def test_constant_mean_fixed_variance(self):
rng = RandomState(1234)
variance = 2 + rng.standard_normal(self.y.shape[0])**2.0
std = np.sqrt(variance)
y = pd.Series(std * rng.standard_normal(self.y_series.shape[0]),
index=self.y_series.index)
mod = ConstantMean(y, volatility=FixedVariance(variance))
res = mod.fit(disp=DISPLAY)
res.summary()
assert len(res.params) == 2
assert "scale" in res.params.index
mod = ARX(self.y_series,
lags=[1, 2, 3],
volatility=FixedVariance(variance))
res = mod.fit(disp=DISPLAY)
assert len(res.params) == 5
assert "scale" in res.params.index
mod = ARX(
self.y_series,
lags=[1, 2, 3],
volatility=FixedVariance(variance, unit_scale=True),
)
res = mod.fit(disp=DISPLAY)
assert len(res.params) == 4
assert "scale" not in res.params.index
def test_constant_mean_fixed_variance(self):
variance = 2 + self.rng.standard_normal(self.y.shape[0]) ** 2.0
mod = ConstantMean(self.y_series, volatility=FixedVariance(variance))
res = mod.fit()
print(res.summary())
assert len(res.params) == 2
assert 'scale' in res.params.index
mod = ARX(self.y_series, lags=[1, 2, 3], volatility=FixedVariance(variance))
res = mod.fit()
assert len(res.params) == 5
assert 'scale' in res.params.index
mod = ARX(self.y_series, lags=[1, 2, 3],
volatility=FixedVariance(variance, unit_scale=True))
res = mod.fit()
assert len(res.params) == 4
assert 'scale' not in res.params.index
def test_fixed_variance(self):
variance = np.arange(1000.0) + 1.0
fv = FixedVariance(variance)
fv.start, fv.stop = 0, 1000
parameters = np.array([2.0])
resids = self.resids
sigma2 = np.empty_like(resids)
backcast = fv.backcast(resids)
var_bounds = fv.variance_bounds(resids, 2.0)
fv.compute_variance(parameters, resids, sigma2, backcast, var_bounds)
sv = fv.starting_values(resids)
cons = fv.constraints()
bounds = fv.bounds(resids)
assert_allclose(sigma2, 2.0 * variance)
assert_allclose(sv, (resids / np.sqrt(variance)).var())
assert var_bounds.shape == (resids.shape[0], 2)
assert fv.num_params == 1
assert fv.parameter_names() == ['scale']
assert fv.name == 'Fixed Variance'
assert_equal(cons[0], np.ones((1, 1)))
assert_equal(cons[1], np.zeros(1))
assert_equal(bounds[0][0], sv[0] / 100000.0)
sigma2 = np.empty(500)
fv.start = 250
fv.stop = 750
fv.compute_variance(parameters, resids[250:750], sigma2, backcast, var_bounds)
assert_allclose(sigma2, 2.0 * variance[250:750])
fv = FixedVariance(variance, unit_scale=True)
fv.start, fv.stop = 0, 1000
sigma2 = np.empty_like(resids)
parameters = np.empty(0)
fv.compute_variance(parameters, resids, sigma2, backcast, var_bounds)
sv = fv.starting_values(resids)
cons = fv.constraints()
bounds = fv.bounds(resids)
assert_allclose(sigma2, variance)
assert_allclose(sv, np.empty(0))
assert fv.num_params == 0
assert fv.parameter_names() == []
assert fv.name == 'Fixed Variance (Unit Scale)'
assert_equal(cons[0], np.empty((0, 0)))
assert_equal(cons[1], np.empty((0)))
assert bounds == []
rng = Normal()
with pytest.raises(NotImplementedError):
fv.simulate(parameters, 1000, rng)
fv = FixedVariance(variance, unit_scale=True)
fv.start, fv.stop = 123, 731
sigma2 = np.empty_like(resids)
parameters = np.empty(0)
assert fv.start == 123
assert fv.stop == 731
with pytest.raises(ValueError):
fv.compute_variance(parameters, resids, sigma2, backcast, var_bounds)
def test_fixed_variance(self):
variance = np.arange(1000.0) + 1.0
fv = FixedVariance(variance)
fv.start, fv.stop = 0, 1000
parameters = np.array([2.0])
resids = self.resids
sigma2 = np.empty_like(resids)
backcast = fv.backcast(resids)
var_bounds = fv.variance_bounds(resids, 2.0)
fv.compute_variance(parameters, resids, sigma2, backcast, var_bounds)
sv = fv.starting_values(resids)
cons = fv.constraints()
bounds = fv.bounds(resids)
assert_allclose(sigma2, 2.0 * variance)
assert_allclose(sv, (resids / np.sqrt(variance)).var())
assert var_bounds.shape == (resids.shape[0], 2)
assert fv.num_params == 1
assert fv.parameter_names() == ['scale']
assert fv.name == 'Fixed Variance'
assert_equal(cons[0], np.ones((1, 1)))
assert_equal(cons[1], np.zeros(1))
assert_equal(bounds[0][0], sv[0] / 100000.0)
sigma2 = np.empty(500)
fv.start = 250
fv.stop = 750
fv.compute_variance(parameters, resids[250:750], sigma2, backcast, var_bounds)
assert_allclose(sigma2, 2.0 * variance[250:750])
fv = FixedVariance(variance, unit_scale=True)
fv.start, fv.stop = 0, 1000
sigma2 = np.empty_like(resids)
parameters = np.empty(0)
fv.compute_variance(parameters, resids, sigma2, backcast, var_bounds)
sv = fv.starting_values(resids)
cons = fv.constraints()
bounds = fv.bounds(resids)
assert_allclose(sigma2, variance)
assert_allclose(sv, np.empty(0))
assert fv.num_params == 0
assert fv.parameter_names() == []
assert fv.name == 'Fixed Variance (Unit Scale)'
assert_equal(cons[0], np.empty((0, 0)))
assert_equal(cons[1], np.empty((0)))
assert bounds == []
rng = Normal()
with pytest.raises(NotImplementedError):
fv.simulate(parameters, 1000, rng)
fv = FixedVariance(variance, unit_scale=True)
fv.start, fv.stop = 123, 731
sigma2 = np.empty_like(resids)
parameters = np.empty(0)
assert fv.start == 123
assert fv.stop == 731
with pytest.raises(ValueError):
fv.compute_variance(parameters, resids, sigma2, backcast, var_bounds)
