def test_residuals(self):
x, var0 = self.generate_data()
var = VAR(2)
var.fit(x)
self.assertEqual(x.shape, var.residuals.shape)
self.assertTrue(np.allclose(var.rescov, np.eye(var.rescov.shape[0]), 1e-2, 1e-2))
def test_fit(self):
x, var0 = self.generate_data()
y = x.copy()
var = VAR(2)
var.fit(x)
# make sure the input remains unchanged
self.assertTrue(np.all(x == y))
# that limit is rather generous, but we don't want tests to fail due to random variation
self.assertTrue(np.all(np.abs(var0.coef - var.coef) < 0.02))
def test_whiteness(self):
r = np.random.randn(100, 5, 10) # gaussian white noise
r0 = r.copy()
var = VAR(0)
var.residuals = r
p = var.test_whiteness(20)
self.assertTrue(np.all(r == r0)) # make sure we don't modify the input
self.assertTrue(p > 0.05) # test should be non-significant for white noise
r[3:, 1, :] = r[:-3, 0, :] # create cross-correlation at lag 3
p = var.test_whiteness(20)
self.assertFalse(p > 0.05) # now test should be significant
def func(self):
x, var0 = self.generate_data()
y = x.copy()
var = VAR(10, o)
var.fit(x)
# make sure the input remains unchanged
self.assertTrue(np.all(x == y))
b0 = np.zeros((2, 20))
b0[:, 0:2] = var0.coef[:, 0:2]
b0[:, 10:12] = var0.coef[:, 2:4]
# that limit is rather generous, but we don't want tests to fail due to random variation
self.assertTrue(np.all(np.abs(b0 - var.coef) < 0.02))
def test_simulate(self):
noisefunc = lambda: [1, 1] # use deterministic function instead of noise
num_samples = 100
b = np.array([[0.2, 0.1, 0.4, -0.1], [0.3, -0.2, 0.1, 0]])
var = VAR(2)
var.coef = b
x = var.simulate(num_samples, noisefunc)
# make sure we got expected values within reasonable accuracy
for n in range(10, num_samples):
self.assertTrue(
np.all(
np.abs(x[n, :] - 1 - np.dot(b[:, 0::2], x[n - 1, :]) - np.dot(b[:, 1::2], x[n - 2, :])) < epsilon
)
)
def generate_data(self):
var = VAR(2)
var.coef = np.array([[0.2, 0.1, 0.4, -0.1], [0.3, -0.2, 0.1, 0]])
l = (1000, 100)
x = var.simulate(l)
return x, var
