def test_arch(self): arch = ARCH() sv = arch.starting_values(self.resids) assert_equal(sv.shape[0], arch.num_params) bounds = arch.bounds(self.resids) assert_equal(bounds[0], (0.0, 10.0 * np.mean(self.resids ** 2.0))) assert_equal(bounds[1], (0.0, 1.0)) backcast = arch.backcast(self.resids) w = 0.94 ** np.arange(75) assert_almost_equal(backcast, np.sum((self.resids[:75] ** 2) * (w / w.sum()))) parameters = np.array([0.5, 0.7]) var_bounds = arch.variance_bounds(self.resids) arch.compute_variance(parameters, self.resids, self.sigma2, backcast, var_bounds) cond_var_direct = np.zeros_like(self.sigma2) rec.arch_recursion(parameters, self.resids, cond_var_direct, 1, self.T, backcast, var_bounds) assert_allclose(self.sigma2, cond_var_direct) a, b = arch.constraints() a_target = np.vstack((np.eye(2), np.array([[0, -1.0]]))) b_target = np.array([0.0, 0.0, -1.0]) assert_array_equal(a, a_target) assert_array_equal(b, b_target) state = self.rng.get_state() rng = Normal() rng.random_state.set_state(state) sim_data = arch.simulate(parameters, self.T, rng.simulate([])) self.rng.set_state(state) e = self.rng.standard_normal(self.T + 500) initial_value = 1.0 sigma2 = np.zeros(self.T + 500) data = np.zeros(self.T + 500) for t in range(self.T + 500): sigma2[t] = parameters[0] shock = initial_value if t == 0 else data[t - 1] ** 2.0 sigma2[t] += parameters[1] * shock data[t] = e[t] * np.sqrt(sigma2[t]) data = data[500:] sigma2 = sigma2[500:] assert_almost_equal(data - sim_data[0] + 1.0, np.ones_like(data)) assert_almost_equal(sigma2 / sim_data[1], np.ones_like(sigma2)) names = arch.parameter_names() names_target = ['omega', 'alpha[1]'] assert_equal(names, names_target) assert_equal(arch.name, 'ARCH') assert_equal(arch.num_params, 2) assert_equal(arch.p, 1) assert isinstance(arch.__str__(), str) txt = arch.__repr__() assert str(hex(id(arch))) in txt
def test_arch(self): arch = ARCH() sv = arch.starting_values(self.resids) assert_equal(sv.shape[0], arch.num_params) bounds = arch.bounds(self.resids) assert_equal(bounds[0], (0.0, 10.0 * np.mean(self.resids ** 2.0))) assert_equal(bounds[1], (0.0, 1.0)) backcast = arch.backcast(self.resids) w = 0.94 ** np.arange(75) assert_almost_equal(backcast, np.sum((self.resids[:75] ** 2) * (w / w.sum()))) parameters = np.array([0.5, 0.7]) var_bounds = arch.variance_bounds(self.resids) arch.compute_variance(parameters, self.resids, self.sigma2, backcast, var_bounds) cond_var_direct = np.zeros_like(self.sigma2) rec.arch_recursion(parameters, self.resids, cond_var_direct, 1, self.T, backcast, var_bounds) assert_allclose(self.sigma2, cond_var_direct) A, b = arch.constraints() A_target = np.vstack((np.eye(2), np.array([[0, -1.0]]))) b_target = np.array([0.0, 0.0, -1.0]) assert_array_equal(A, A_target) assert_array_equal(b, b_target) state = np.random.get_state() rng = Normal() sim_data = arch.simulate(parameters, self.T, rng.simulate([])) np.random.set_state(state) e = np.random.standard_normal(self.T + 500) initial_value = 1.0 sigma2 = np.zeros(self.T + 500) data = np.zeros(self.T + 500) for t in range(self.T + 500): sigma2[t] = parameters[0] shock = initial_value if t == 0 else data[t - 1] ** 2.0 sigma2[t] += parameters[1] * shock data[t] = e[t] * np.sqrt(sigma2[t]) data = data[500:] sigma2 = sigma2[500:] assert_almost_equal(data - sim_data[0] + 1.0, np.ones_like(data)) assert_almost_equal(sigma2 / sim_data[1], np.ones_like(sigma2)) names = arch.parameter_names() names_target = ['omega', 'alpha[1]'] assert_equal(names, names_target) assert_equal(arch.name, 'ARCH') assert_equal(arch.num_params, 2) assert_equal(arch.p, 1) assert_true(isinstance(arch.__str__(), str)) repr = arch.__repr__() assert_true(str(hex(id(arch))) in repr)
def test_arch_harch(self): arch = ARCH(p=1) harch = HARCH(lags=1) assert_equal(arch.num_params, harch.num_params) parameters = np.array([0.5, 0.5]) backcast = arch.backcast(self.resids) assert_equal(backcast, harch.backcast(self.resids)) sigma2_arch = np.zeros_like(self.sigma2) sigma2_harch = np.zeros_like(self.sigma2) var_bounds = arch.variance_bounds(self.resids) arch.compute_variance(parameters, self.resids, sigma2_arch, backcast, var_bounds) harch.compute_variance(parameters, self.resids, sigma2_harch, backcast, var_bounds) assert_allclose(sigma2_arch, sigma2_harch) a, b = arch.constraints() ah, bh = harch.constraints() assert_equal(a, ah) assert_equal(b, bh) assert isinstance(arch.__str__(), str) txt = arch.__repr__() assert str(hex(id(arch))) in txt
def test_arch_harch(self): arch = ARCH(p=1) harch = HARCH(lags=1) assert_equal(arch.num_params, harch.num_params) parameters = np.array([0.5, 0.5]) backcast = arch.backcast(self.resids) assert_equal(backcast, harch.backcast(self.resids)) sigma2_arch = np.zeros_like(self.sigma2) sigma2_harch = np.zeros_like(self.sigma2) var_bounds = arch.variance_bounds(self.resids) arch.compute_variance(parameters, self.resids, sigma2_arch, backcast, var_bounds) harch.compute_variance(parameters, self.resids, sigma2_harch, backcast, var_bounds) assert_allclose(sigma2_arch, sigma2_harch) A, b = arch.constraints() Ah, bh = harch.constraints() assert_equal(A, Ah) assert_equal(b, bh) assert_true(isinstance(arch.__str__(), str)) repr = arch.__repr__() assert_true(str(hex(id(arch))) in repr)