def test_studentst(self):
dist = StudentsT()
v = 4.0
ll1 = dist.loglikelihood(np.array([v]), self.resids, self.sigma2)
# Direct calculation of PDF, then log
constant = np.exp(gammaln(0.5 * (v + 1)) - gammaln(0.5 * v))
pdf = constant / np.sqrt(np.pi * (v - 2) * self.sigma2)
pdf *= (1 + self.resids**2.0 / (self.sigma2 * (v - 2)))**(-(v + 1) / 2)
ll2 = np.log(pdf).sum()
assert_almost_equal(ll1, ll2)
assert_equal(dist.num_params, 1)
bounds = dist.bounds(self.resids)
assert_equal(len(bounds), 1)
a, b = dist.constraints()
assert_equal(a.shape, (2, 1))
k = stats.kurtosis(self.resids, fisher=False)
sv = max((4.0 * k - 6.0) / (k - 3.0) if k > 3.75 else 12.0, 4.0)
assert_array_equal(dist.starting_values(self.resids), np.array([sv]))
with pytest.raises(ValueError):
dist.simulate(np.array([1.5]))
def test_studentst(self):
dist = StudentsT()
v = 4.0
ll1 = dist.loglikelihood(np.array([v]), self.resids, self.sigma2)
# Direct calculation of PDF, then log
constant = np.exp(gammaln(0.5 * (v + 1)) - gammaln(0.5 * v))
pdf = constant / np.sqrt(np.pi * (v - 2) * self.sigma2)
pdf *= (1 + self.resids ** 2.0 / (self.sigma2 * (v - 2))) ** (
-(v + 1) / 2)
ll2 = np.log(pdf).sum()
assert_almost_equal(ll1, ll2)
assert_equal(dist.num_params, 1)
bounds = dist.bounds(self.resids)
assert_equal(len(bounds), 1)
a, b = dist.constraints()
assert_equal(a.shape, (2, 1))
k = stats.kurtosis(self.resids, fisher=False)
sv = max((4.0 * k - 6.0) / (k - 3.0) if k > 3.75 else 12.0, 4.0)
assert_array_equal(dist.starting_values(self.resids), np.array([sv]))
with pytest.raises(ValueError):
dist.simulate(np.array([1.5]))
def test_warnings(self):
garch = GARCH()
parameters = np.array([0.1, 0.2, 0.8, 4.0])
studt = StudentsT()
with warnings.catch_warnings(record=True) as w:
garch.simulate(parameters, 1000, studt.simulate([4.0]))
assert_equal(len(w), 1)
harch = HARCH(lags=[1, 5, 22])
parameters = np.array([0.1, 0.2, 0.4, 0.5])
with warnings.catch_warnings(record=True) as w:
harch.simulate(parameters, 1000, studt.simulate([4.0]))
assert_equal(len(w), 1)
def test_warnings(self):
garch = GARCH()
parameters = np.array([0.1, 0.2, 0.8, 4.0])
studt = StudentsT()
with warnings.catch_warnings(record=True) as w:
garch.simulate(parameters, 1000, studt.simulate([4.0]))
assert_equal(len(w), 1)
harch = HARCH(lags=[1, 5, 22])
parameters = np.array([0.1, 0.2, 0.4, 0.5])
with warnings.catch_warnings(record=True) as w:
harch.simulate(parameters, 1000, studt.simulate([4.0]))
assert_equal(len(w), 1)
def test_warnings_nonstationary(self):
garch = GARCH()
parameters = np.array([0.1, 0.2, 0.8, 4.0])
studt = StudentsT()
warnings.simplefilter('always', UserWarning)
with pytest.warns(InitialValueWarning):
garch.simulate(parameters, 1000, studt.simulate([4.0]))
def test_warnings_nonstationary_harch(self):
studt = StudentsT()
harch = HARCH(lags=[1, 5, 22])
parameters = np.array([0.1, 0.2, 0.4, 0.5])
with pytest.warns(InitialValueWarning):
harch.simulate(parameters, 1000, studt.simulate([4.0]))
