def test_whiteness(self):
np.random.seed(91)
var = VARBase(0)
var.residuals = np.random.randn(10, 5, 100)
pr = sp.plot_whiteness(var, 20, repeats=100)
self.assertGreater(pr, 0.05)
def test_whiteness(self):
np.random.seed(91)
var = VARBase(0)
var.residuals = np.random.randn(10, 5, 100)
pr = sp.plot_whiteness(var, 20, repeats=100)
self.assertGreater(pr, 0.05)
def test_whiteness(self):
np.random.seed(91)
r = np.random.randn(80, 15, 100) # gaussian white noise
r0 = r.copy()
var = VAR(0, n_jobs=-1)
var.residuals = r
p = var.test_whiteness(20, random_state=1)
self.assertTrue(np.all(r == r0)) # make sure we don't modify the input
self.assertGreater(p, 0.01) # test should be non-significant for white noise
r[:, 1, 3:] = r[:, 0, :-3] # create cross-correlation at lag 3
p = var.test_whiteness(20)
self.assertLessEqual(p, 0.01) # now test should be significant
def test_whiteness(self):
np.random.seed(91)
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.assertGreater(
p, 0.01) # 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.assertLessEqual(p, 0.01) # now test should be significant
