def test_zero_mean(self):
zm = ZeroMean(self.y)
parameters = np.array([1.0])
data = zm.simulate(parameters, self.T)
assert_equal(data.shape, (self.T, 3))
assert_equal(data['data'].shape[0], self.T)
assert_equal(zm.num_params, 0)
bounds = zm.bounds()
assert_equal(bounds, [])
assert_equal(zm.constant, False)
a, b = zm.constraints()
assert_equal(a, np.empty((0, 0)))
assert_equal(b, np.empty((0,)))
assert isinstance(zm.volatility, ConstantVariance)
assert isinstance(zm.distribution, Normal)
assert_equal(zm.lags, None)
res = zm.fit(disp='off')
assert_almost_equal(res.params, np.array([np.mean(self.y ** 2)]))
forecasts = res.forecast(horizon=99)
direct = pd.DataFrame(index=np.arange(self.y.shape[0]),
columns=['h.{0:>02d}'.format(i + 1) for i in
range(99)],
dtype=np.float64)
direct.iloc[:, :] = 0.0
assert isinstance(forecasts, ARCHModelForecast)
# TODO
# assert_frame_equal(direct, forecasts)
garch = GARCH()
zm.volatility = garch
zm.fit(update_freq=0, disp=DISPLAY)
assert isinstance(zm.__repr__(), str)
assert isinstance(zm.__str__(), str)
assert '<strong>' in zm._repr_html_()
def setup_class(cls):
cls.rng = RandomState(1234)
cls.T = 1000
cls.resids = cls.rng.standard_normal(cls.T)
zm = ZeroMean()
zm.volatility = GARCH()
seed = 12345
random_state = np.random.RandomState(seed)
zm.distribution = Normal(random_state=random_state)
sim_data = zm.simulate(np.array([0.1, 0.1, 0.8]), 1000)
with pytest.raises(ValueError):
zm.simulate(np.array([0.1, 0.1, 0.8]), 1000, initial_value=3.0)
date_index = pd.date_range("2000-12-31", periods=1000, freq="W")
cls.y = sim_data.data.values
cls.y_df = pd.DataFrame(
cls.y[:, None], columns=["LongVariableName"], index=date_index
)
cls.y_series = pd.Series(
cls.y, name="VeryVeryLongLongVariableName", index=date_index
)
x = cls.resids + cls.rng.standard_normal(cls.T)
cls.x = x[:, None]
cls.x_df = pd.DataFrame(cls.x, columns=["LongExogenousName"])
cls.resid_var = np.var(cls.resids)
cls.sigma2 = np.zeros_like(cls.resids)
cls.backcast = 1.0
def test_zero_mean(self):
zm = ZeroMean(self.y)
parameters = np.array([1.0])
data = zm.simulate(parameters, self.T)
assert_equal(data.shape, (self.T, 3))
assert_equal(data['data'].shape[0], self.T)
assert_equal(zm.num_params, 0)
bounds = zm.bounds()
assert_equal(bounds, [])
assert_equal(zm.constant, False)
a, b = zm.constraints()
assert_equal(a, np.empty((0, 0)))
assert_equal(b, np.empty((0,)))
assert isinstance(zm.volatility, ConstantVariance)
assert isinstance(zm.distribution, Normal)
assert_equal(zm.lags, None)
res = zm.fit(disp='off')
assert_almost_equal(res.params, np.array([np.mean(self.y ** 2)]))
forecasts = res.forecast(horizon=99)
direct = pd.DataFrame(index=np.arange(self.y.shape[0]),
columns=['h.{0:>02d}'.format(i + 1) for i in
range(99)],
dtype=np.float64)
direct.iloc[:, :] = 0.0
assert isinstance(forecasts, ARCHModelForecast)
# TODO
# assert_frame_equal(direct, forecasts)
garch = GARCH()
zm.volatility = garch
zm.fit(update_freq=0, disp=DISPLAY)
def setup_class(cls):
cls.rng = RandomState(1234)
cls.T = 1000
cls.resids = cls.rng.randn(cls.T)
zm = ZeroMean()
zm.volatility = GARCH()
sim_data = zm.simulate(np.array([0.1, 0.1, 0.8]), 1000)
date_index = pd.date_range('2000-12-31', periods=1000, freq='W')
cls.y = sim_data.data.values
cls.y_df = pd.DataFrame(cls.y[:, None],
columns=['LongVariableName'],
index=date_index)
cls.y_series = pd.Series(cls.y,
name='VeryVeryLongLongVariableName',
index=date_index)
x = cls.resids + cls.rng.randn(cls.T)
cls.x = x[:, None]
cls.x_df = pd.DataFrame(cls.x, columns=['LongExogenousName'])
cls.resid_var = np.var(cls.resids)
cls.sigma2 = np.zeros_like(cls.resids)
cls.backcast = 1.0
def setup_class(cls):
np.random.seed(1234)
cls.T = 1000
cls.resids = randn(cls.T)
np.random.seed(1234)
zm = ZeroMean()
zm.volatility = GARCH()
sim_data = zm.simulate(np.array([0.1, 0.1, 0.8]), 1000)
date_index = pd.date_range('2000-12-31', periods=1000, freq='W')
cls.y = sim_data.data.values
cls.y_df = pd.DataFrame(cls.y[:, None],
columns=['LongVariableName'],
index=date_index)
cls.y_series = pd.Series(cls.y,
name='VeryVeryLongLongVariableName',
index=date_index)
x = cls.resids + randn(cls.T)
cls.x = x[:, None]
cls.x_df = pd.DataFrame(cls.x, columns=['LongExogenousName'])
cls.resid_var = np.var(cls.resids)
cls.sigma2 = np.zeros_like(cls.resids)
cls.backcast = 1.0
