def test_invalid(): # Invalid initializations (also tests for some invalid calls to set) assert_raises(ValueError, Initialization, 5, '') assert_raises(ValueError, Initialization, 5, 'stationary', constant=[1, 2]) assert_raises(ValueError, Initialization, 5, 'stationary', stationary_cov=[1, 2]) assert_raises(ValueError, Initialization, 5, 'known') assert_raises(ValueError, Initialization, 5, 'known', constant=[1]) assert_raises(ValueError, Initialization, 5, 'known', stationary_cov=[0]) # Invalid set() / unset() calls init = Initialization(5) assert_raises(ValueError, init.set, -1, 'diffuse') assert_raises(ValueError, init.unset, -1) assert_raises(ValueError, init.set, 5, 'diffuse') assert_raises(ValueError, init.set, 'x', 'diffuse') assert_raises(ValueError, init.unset, 'x') assert_raises(ValueError, init.set, (1, 2, 3), 'diffuse') assert_raises(ValueError, init.unset, (1, 2, 3)) init.set(None, 'diffuse') assert_raises(ValueError, init.set, 1, 'diffuse') init.clear() init.set(1, 'diffuse') assert_raises(ValueError, init.set, None, 'stationary') init.clear() assert_raises(ValueError, init.unset, 1) # Invalid __call__ init = Initialization(2) assert_raises(ValueError, init) init = Initialization(2, 'stationary') assert_raises(ValueError, init)
def test_mixed_stationary(): # More specific tests when one or more blocks are initialized as stationary endog = np.zeros(10) mod = sarimax.SARIMAX(endog, order=(2, 1, 0)) phi = [0.5, -0.2] sigma2 = 2. mod.update(np.r_[phi, sigma2]) init = Initialization(mod.k_states) init.set(0, 'diffuse') init.set((1, 3), 'stationary') desired_cov = np.zeros((3, 3)) T = np.array([[0.5, 1], [-0.2, 0]]) Q = np.diag([sigma2, 0]) desired_cov[1:, 1:] = solve_discrete_lyapunov(T, Q) check_initialization(mod, init, [0, 0, 0], np.diag([1, 0, 0]), desired_cov) init.clear() init.set(0, 'diffuse') init.set(1, 'stationary') init.set(2, 'approximate_diffuse') T = np.array([[0.5]]) Q = np.diag([sigma2]) desired_cov = np.diag([0, np.squeeze(solve_discrete_lyapunov(T, Q)), 1e6]) check_initialization(mod, init, [0, 0, 0], np.diag([1, 0, 0]), desired_cov) init.clear() init.set(0, 'diffuse') init.set(1, 'stationary') init.set(2, 'stationary') desired_cov[2, 2] = 0 check_initialization(mod, init, [0, 0, 0], np.diag([1, 0, 0]), desired_cov) # Test with a VAR model endog = np.zeros((10, 2)) mod = varmax.VARMAX(endog, order=(1, 0), ) intercept = [1.5, -0.1] transition = np.array([[0.5, -0.2], [0.1, 0.8]]) cov = np.array([[1.2, -0.4], [-0.4, 0.4]]) tril = np.tril_indices(2) params = np.r_[intercept, transition.ravel(), np.linalg.cholesky(cov)[tril]] mod.update(params) # > stationary, global init = Initialization(mod.k_states, 'stationary') desired_intercept = np.linalg.solve(np.eye(2) - transition, intercept) desired_cov = solve_discrete_lyapunov(transition, cov) check_initialization(mod, init, desired_intercept, np.diag([0, 0]), desired_cov) # > diffuse, global init.set(None, 'diffuse') check_initialization(mod, init, [0, 0], np.eye(2), np.diag([0, 0])) # > stationary, individually init.unset(None) init.set(0, 'stationary') init.set(1, 'stationary') a, Pinf, Pstar = init(model=mod) desired_intercept = [intercept[0] / (1 - transition[0, 0]), intercept[1] / (1 - transition[1, 1])] desired_cov = np.diag([cov[0, 0] / (1 - transition[0, 0]**2), cov[1, 1] / (1 - transition[1, 1]**2)]) check_initialization(mod, init, desired_intercept, np.diag([0, 0]), desired_cov)
def test_mixed_stationary(): # More specific tests when one or more blocks are initialized as stationary endog = np.zeros(10) mod = sarimax.SARIMAX(endog, order=(2, 1, 0)) phi = [0.5, -0.2] sigma2 = 2. mod.update(np.r_[phi, sigma2]) init = Initialization(mod.k_states) init.set(0, 'diffuse') init.set((1, 3), 'stationary') desired_cov = np.zeros((3, 3)) T = np.array([[0.5, 1], [-0.2, 0]]) Q = np.diag([sigma2, 0]) desired_cov[1:, 1:] = solve_discrete_lyapunov(T, Q) check_initialization(mod, init, [0, 0, 0], np.diag([1, 0, 0]), desired_cov) init.clear() init.set(0, 'diffuse') init.set(1, 'stationary') init.set(2, 'approximate_diffuse') T = np.array([[0.5]]) Q = np.diag([sigma2]) desired_cov = np.diag([0, np.squeeze(solve_discrete_lyapunov(T, Q)), 1e6]) check_initialization(mod, init, [0, 0, 0], np.diag([1, 0, 0]), desired_cov) init.clear() init.set(0, 'diffuse') init.set(1, 'stationary') init.set(2, 'stationary') desired_cov[2, 2] = 0 check_initialization(mod, init, [0, 0, 0], np.diag([1, 0, 0]), desired_cov) # Test with a VAR model endog = np.zeros((10, 2)) mod = varmax.VARMAX( endog, order=(1, 0), ) intercept = [1.5, -0.1] transition = np.array([[0.5, -0.2], [0.1, 0.8]]) cov = np.array([[1.2, -0.4], [-0.4, 0.4]]) tril = np.tril_indices(2) params = np.r_[intercept, transition.ravel(), np.linalg.cholesky(cov)[tril]] mod.update(params) # > stationary, global init = Initialization(mod.k_states, 'stationary') desired_intercept = np.linalg.solve(np.eye(2) - transition, intercept) desired_cov = solve_discrete_lyapunov(transition, cov) check_initialization(mod, init, desired_intercept, np.diag([0, 0]), desired_cov) # > diffuse, global init.set(None, 'diffuse') check_initialization(mod, init, [0, 0], np.eye(2), np.diag([0, 0])) # > stationary, individually init.unset(None) init.set(0, 'stationary') init.set(1, 'stationary') a, Pinf, Pstar = init(model=mod) desired_intercept = [ intercept[0] / (1 - transition[0, 0]), intercept[1] / (1 - transition[1, 1]) ] desired_cov = np.diag([ cov[0, 0] / (1 - transition[0, 0]**2), cov[1, 1] / (1 - transition[1, 1]**2) ]) check_initialization(mod, init, desired_intercept, np.diag([0, 0]), desired_cov)