def test_constant_mean(self):
cm = ConstantMean(self.y)
parameters = np.array([5.0, 1.0])
cm.simulate(parameters, self.T)
assert_equal(cm.num_params, 1)
bounds = cm.bounds()
assert_equal(bounds, [(-np.inf, np.inf)])
assert_equal(cm.constant, True)
a, b = cm.constraints()
assert_equal(a, np.empty((0, 1)))
assert_equal(b, np.empty((0,)))
assert isinstance(cm.volatility, ConstantVariance)
assert isinstance(cm.distribution, Normal)
assert_equal(cm.lags, None)
res = cm.fit(disp='off')
expected = np.array([self.y.mean(), self.y.var()])
assert_almost_equal(res.params, expected)
forecasts = res.forecast(horizon=20, start=20)
direct = pd.DataFrame(index=np.arange(self.y.shape[0]),
columns=['h.{0:>02d}'.format(i + 1) for i in
range(20)],
dtype=np.float64)
direct.iloc[20:, :] = res.params.iloc[0]
# TODO
# assert_frame_equal(direct, forecasts)
assert isinstance(forecasts, ARCHModelForecast)
def test_constant_mean(self):
cm = ConstantMean(self.y)
parameters = np.array([5.0, 1.0])
cm.simulate(parameters, self.T)
assert_equal(cm.num_params, 1)
with pytest.raises(ValueError):
cm.simulate(parameters, self.T, x=np.array(10))
bounds = cm.bounds()
assert_equal(bounds, [(-np.inf, np.inf)])
assert_equal(cm.constant, True)
a, b = cm.constraints()
assert_equal(a, np.empty((0, 1)))
assert_equal(b, np.empty((0,)))
assert isinstance(cm.volatility, ConstantVariance)
assert isinstance(cm.distribution, Normal)
assert_equal(cm.lags, None)
res = cm.fit(disp='off')
expected = np.array([self.y.mean(), self.y.var()])
assert_almost_equal(res.params, expected)
forecasts = res.forecast(horizon=20, start=20)
direct = pd.DataFrame(index=np.arange(self.y.shape[0]),
columns=['h.{0:>02d}'.format(i + 1) for i in
range(20)],
dtype=np.float64)
direct.iloc[20:, :] = res.params.iloc[0]
# TODO
# assert_frame_equal(direct, forecasts)
assert isinstance(forecasts, ARCHModelForecast)
assert isinstance(cm.__repr__(), str)
assert isinstance(cm.__str__(), str)
assert '<strong>' in cm._repr_html_()
def test_constant_mean(self):
cm = ConstantMean(self.y)
parameters = np.array([5.0, 1.0])
cm.simulate(parameters, self.T)
assert_equal(cm.num_params, 1)
bounds = cm.bounds()
assert_equal(bounds, [(-np.inf, np.inf)])
assert_equal(cm.constant, True)
a, b = cm.constraints()
assert_equal(a, np.empty((0, 1)))
assert_equal(b, np.empty((0, )))
assert_true(isinstance(cm.volatility, ConstantVariance))
assert_true(isinstance(cm.distribution, Normal))
assert_equal(cm.first_obs, 0)
assert_equal(cm.last_obs, 1000)
assert_equal(cm.lags, None)
res = cm.fit()
assert_almost_equal(res.params, np.array([self.y.mean(),
self.y.var()]))
forecasts = res.forecast(horizon=20, start=20)
direct = pd.DataFrame(
index=np.arange(self.y.shape[0]),
columns=['h.{0:>02d}'.format(i + 1) for i in range(20)],
dtype=np.float64)
direct.iloc[20:, :] = res.params.iloc[0]
assert_frame_equal(direct, forecasts)