def test_stable(self):
var = VAR(1)
# Stable AR model -- rule of thumb: sum(coefs) < 1
var.coef = np.asarray([[0.5, 0.3]])
self.assertTrue(var.is_stable())
# Unstable AR model -- rule of thumb: sum(coefs) > 1
var.coef = np.asarray([[0.5, 0.7]])
self.assertFalse(var.is_stable())
def generate_data(self, cc=((1, 0), (0, 1))):
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, lambda: np.random.randn(2).dot(cc))
self.assertEqual(x.shape, (l[1], 2, l[0]))
return x, var
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
np.random.seed(42)
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, :])) < 1e-10))
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
np.random.seed(42)
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, :])) < 1e-10))
def generate_data(self, cc=((1, 0), (0, 1))):
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, lambda: np.random.randn(2).dot(cc))
return x, var
