class Clark1987(object): """ Clark's (1987) univariate unobserved components model of real GDP (as presented in Kim and Nelson, 1999) Test data produced using GAUSS code described in Kim and Nelson (1999) and found at http://econ.korea.ac.kr/~cjkim/SSMARKOV.htm See `results.results_kalman_filter` for more information. """ def __init__(self, dtype=float, alternate_timing=False, **kwargs): self.true = results_kalman_filter.uc_uni self.true_states = pd.DataFrame(self.true['states']) # GDP, Quarterly, 1947.1 - 1995.3 data = pd.DataFrame(self.true['data'], index=pd.date_range('1947-01-01', '1995-07-01', freq='QS'), columns=['GDP']) data['lgdp'] = np.log(data['GDP']) # Construct the statespace representation k_states = 4 self.model = Model(data['lgdp'], k_states=k_states, **kwargs) self.model.design[:, :, 0] = [1, 1, 0, 0] self.model.transition[([0, 0, 1, 1, 2, 3], [0, 3, 1, 2, 1, 3], [0, 0, 0, 0, 0, 0])] = [1, 1, 0, 0, 1, 1] self.model.selection = np.eye(self.model.k_states) # Update matrices with given parameters (sigma_v, sigma_e, sigma_w, phi_1, phi_2) = np.array(self.true['parameters']) self.model.transition[([1, 1], [1, 2], [0, 0])] = [phi_1, phi_2] self.model.state_cov[np.diag_indices(k_states) + (np.zeros(k_states, dtype=int), )] = [ sigma_v**2, sigma_e**2, 0, sigma_w**2 ] # Initialization initial_state = np.zeros((k_states, )) initial_state_cov = np.eye(k_states) * 100 # Initialization: modification if not alternate_timing: initial_state_cov = np.dot( np.dot(self.model.transition[:, :, 0], initial_state_cov), self.model.transition[:, :, 0].T) else: self.model.timing_init_filtered = True self.model.initialize_known(initial_state, initial_state_cov) def run_filter(self): # Filter the data self.results = self.model.filter() def test_loglike(self): assert_almost_equal(self.results.llf_obs[self.true['start']:].sum(), self.true['loglike'], 5) def test_filtered_state(self): assert_almost_equal( self.results.filtered_state[0][self.true['start']:], self.true_states.iloc[:, 0], 4) assert_almost_equal( self.results.filtered_state[1][self.true['start']:], self.true_states.iloc[:, 1], 4) assert_almost_equal( self.results.filtered_state[3][self.true['start']:], self.true_states.iloc[:, 2], 4)
class Clark1987(object): """ Clark's (1987) univariate unobserved components model of real GDP (as presented in Kim and Nelson, 1999) Test data produced using GAUSS code described in Kim and Nelson (1999) and found at http://econ.korea.ac.kr/~cjkim/SSMARKOV.htm See `results.results_kalman_filter` for more information. """ def __init__(self, dtype=float, alternate_timing=False, **kwargs): self.true = results_kalman_filter.uc_uni self.true_states = pd.DataFrame(self.true['states']) # GDP, Quarterly, 1947.1 - 1995.3 data = pd.DataFrame( self.true['data'], index=pd.date_range('1947-01-01', '1995-07-01', freq='QS'), columns=['GDP'] ) data['lgdp'] = np.log(data['GDP']) # Construct the statespace representation k_states = 4 self.model = Model(data['lgdp'], k_states=k_states, **kwargs) self.model.design[:, :, 0] = [1, 1, 0, 0] self.model.transition[([0, 0, 1, 1, 2, 3], [0, 3, 1, 2, 1, 3], [0, 0, 0, 0, 0, 0])] = [1, 1, 0, 0, 1, 1] self.model.selection = np.eye(self.model.k_states) # Update matrices with given parameters (sigma_v, sigma_e, sigma_w, phi_1, phi_2) = np.array( self.true['parameters'] ) self.model.transition[([1, 1], [1, 2], [0, 0])] = [phi_1, phi_2] self.model.state_cov[ np.diag_indices(k_states)+(np.zeros(k_states, dtype=int),)] = [ sigma_v**2, sigma_e**2, 0, sigma_w**2 ] # Initialization initial_state = np.zeros((k_states,)) initial_state_cov = np.eye(k_states)*100 # Initialization: modification if not alternate_timing: initial_state_cov = np.dot( np.dot(self.model.transition[:, :, 0], initial_state_cov), self.model.transition[:, :, 0].T ) else: self.model.timing_init_filtered = True self.model.initialize_known(initial_state, initial_state_cov) def run_filter(self): # Filter the data self.results = self.model.filter() def test_loglike(self): assert_almost_equal( self.results.llf_obs[self.true['start']:].sum(), self.true['loglike'], 5 ) def test_filtered_state(self): assert_almost_equal( self.results.filtered_state[0][self.true['start']:], self.true_states.iloc[:, 0], 4 ) assert_almost_equal( self.results.filtered_state[1][self.true['start']:], self.true_states.iloc[:, 1], 4 ) assert_almost_equal( self.results.filtered_state[3][self.true['start']:], self.true_states.iloc[:, 2], 4 )
class Clark1989(object): """ Clark's (1989) bivariate unobserved components model of real GDP (as presented in Kim and Nelson, 1999) Tests two-dimensional observation data. Test data produced using GAUSS code described in Kim and Nelson (1999) and found at http://econ.korea.ac.kr/~cjkim/SSMARKOV.htm See `results.results_kalman_filter` for more information. """ def __init__(self, dtype=float, alternate_timing=False, **kwargs): self.true = results_kalman_filter.uc_bi self.true_states = pd.DataFrame(self.true['states']) # GDP and Unemployment, Quarterly, 1948.1 - 1995.3 data = pd.DataFrame(self.true['data'], index=pd.date_range('1947-01-01', '1995-07-01', freq='QS'), columns=['GDP', 'UNEMP'])[4:] data['GDP'] = np.log(data['GDP']) data['UNEMP'] = (data['UNEMP'] / 100) k_states = 6 self.model = Model(data, k_states=k_states, **kwargs) # Statespace representation self.model.design[:, :, 0] = [[1, 1, 0, 0, 0, 0], [0, 0, 0, 0, 0, 1]] self.model.transition[([0, 0, 1, 1, 2, 3, 4, 5], [0, 4, 1, 2, 1, 2, 4, 5], [0, 0, 0, 0, 0, 0, 0, 0])] = [1, 1, 0, 0, 1, 1, 1, 1] self.model.selection = np.eye(self.model.k_states) # Update matrices with given parameters (sigma_v, sigma_e, sigma_w, sigma_vl, sigma_ec, phi_1, phi_2, alpha_1, alpha_2, alpha_3) = np.array(self.true['parameters'], ) self.model.design[([1, 1, 1], [1, 2, 3], [0, 0, 0])] = [alpha_1, alpha_2, alpha_3] self.model.transition[([1, 1], [1, 2], [0, 0])] = [phi_1, phi_2] self.model.obs_cov[1, 1, 0] = sigma_ec**2 self.model.state_cov[np.diag_indices(k_states) + (np.zeros(k_states, dtype=int), )] = [ sigma_v**2, sigma_e**2, 0, 0, sigma_w**2, sigma_vl**2 ] # Initialization initial_state = np.zeros((k_states, )) initial_state_cov = np.eye(k_states) * 100 # Initialization: modification if not alternate_timing: initial_state_cov = np.dot( np.dot(self.model.transition[:, :, 0], initial_state_cov), self.model.transition[:, :, 0].T) else: self.model.timing_init_filtered = True self.model.initialize_known(initial_state, initial_state_cov) def run_filter(self): # Filter the data self.results = self.model.filter() def test_loglike(self): assert_allclose( # self.results.llf_obs[self.true['start']:].sum(), self.results.llf_obs[0:].sum(), self.true['loglike'], rtol=1e-4, atol=1e-4) def test_filtered_state(self): assert_almost_equal( self.results.filtered_state[0][self.true['start']:], self.true_states.iloc[:, 0], 4) assert_almost_equal( self.results.filtered_state[1][self.true['start']:], self.true_states.iloc[:, 1], 4) assert_almost_equal( self.results.filtered_state[4][self.true['start']:], self.true_states.iloc[:, 2], 4) assert_almost_equal( self.results.filtered_state[5][self.true['start']:], self.true_states.iloc[:, 3], 4)
class Clark1989(object): """ Clark's (1989) bivariate unobserved components model of real GDP (as presented in Kim and Nelson, 1999) Tests two-dimensional observation data. Test data produced using GAUSS code described in Kim and Nelson (1999) and found at http://econ.korea.ac.kr/~cjkim/SSMARKOV.htm See `results.results_kalman_filter` for more information. """ def __init__(self, dtype=float, alternate_timing=False, **kwargs): self.true = results_kalman_filter.uc_bi self.true_states = pd.DataFrame(self.true['states']) # GDP and Unemployment, Quarterly, 1948.1 - 1995.3 data = pd.DataFrame( self.true['data'], index=pd.date_range('1947-01-01', '1995-07-01', freq='QS'), columns=['GDP', 'UNEMP'] )[4:] data['GDP'] = np.log(data['GDP']) data['UNEMP'] = (data['UNEMP']/100) k_states = 6 self.model = Model(data, k_states=k_states, **kwargs) # Statespace representation self.model.design[:, :, 0] = [[1, 1, 0, 0, 0, 0], [0, 0, 0, 0, 0, 1]] self.model.transition[ ([0, 0, 1, 1, 2, 3, 4, 5], [0, 4, 1, 2, 1, 2, 4, 5], [0, 0, 0, 0, 0, 0, 0, 0]) ] = [1, 1, 0, 0, 1, 1, 1, 1] self.model.selection = np.eye(self.model.k_states) # Update matrices with given parameters (sigma_v, sigma_e, sigma_w, sigma_vl, sigma_ec, phi_1, phi_2, alpha_1, alpha_2, alpha_3) = np.array( self.true['parameters'], ) self.model.design[([1, 1, 1], [1, 2, 3], [0, 0, 0])] = [ alpha_1, alpha_2, alpha_3 ] self.model.transition[([1, 1], [1, 2], [0, 0])] = [phi_1, phi_2] self.model.obs_cov[1, 1, 0] = sigma_ec**2 self.model.state_cov[ np.diag_indices(k_states)+(np.zeros(k_states, dtype=int),)] = [ sigma_v**2, sigma_e**2, 0, 0, sigma_w**2, sigma_vl**2 ] # Initialization initial_state = np.zeros((k_states,)) initial_state_cov = np.eye(k_states)*100 # Initialization: modification if not alternate_timing: initial_state_cov = np.dot( np.dot(self.model.transition[:, :, 0], initial_state_cov), self.model.transition[:, :, 0].T ) else: self.model.timing_init_filtered = True self.model.initialize_known(initial_state, initial_state_cov) def run_filter(self): # Filter the data self.results = self.model.filter() def test_loglike(self): assert_allclose( # self.results.llf_obs[self.true['start']:].sum(), self.results.llf_obs[0:].sum(), self.true['loglike'], rtol=1e-4, atol=1e-4 ) def test_filtered_state(self): assert_almost_equal( self.results.filtered_state[0][self.true['start']:], self.true_states.iloc[:, 0], 4 ) assert_almost_equal( self.results.filtered_state[1][self.true['start']:], self.true_states.iloc[:, 1], 4 ) assert_almost_equal( self.results.filtered_state[4][self.true['start']:], self.true_states.iloc[:, 2], 4 ) assert_almost_equal( self.results.filtered_state[5][self.true['start']:], self.true_states.iloc[:, 3], 4 )