def test_summary():
dates = pd.date_range(start='1980-01-01', end='1984-01-01', freq='AS')
endog = pd.Series([1,2,3,4,5], index=dates)
mod = MLEModel(endog, **kwargs)
res = mod.filter([])
# Get the summary
txt = str(res.summary())
# Test res.summary when the model has dates
assert_equal(re.search('Sample:\s+01-01-1980', txt) is not None, True)
assert_equal(re.search('\s+- 01-01-1984', txt) is not None, True)
# Test res.summary when `model_name` was not provided
assert_equal(re.search('Model:\s+MLEModel', txt) is not None, True)
# Smoke test that summary still works when diagnostic tests fail
with warnings.catch_warnings():
warnings.simplefilter("ignore")
res.filter_results._standardized_forecasts_error[:] = np.nan
res.summary()
res.filter_results._standardized_forecasts_error = 1
res.summary()
res.filter_results._standardized_forecasts_error = 'a'
res.summary()
def setup_class(cls, which='mixed', *args, **kwargs):
# Data
dta = datasets.macrodata.load_pandas().data
dta.index = pd.date_range(start='1959-01-01', end='2009-7-01', freq='QS')
obs = np.log(dta[['realgdp','realcons','realinv']]).diff().iloc[1:] * 400
if which == 'all':
obs.iloc[:50, :] = np.nan
obs.iloc[119:130, :] = np.nan
elif which == 'partial':
obs.iloc[0:50, 0] = np.nan
obs.iloc[119:130, 0] = np.nan
elif which == 'mixed':
obs.iloc[0:50, 0] = np.nan
obs.iloc[19:70, 1] = np.nan
obs.iloc[39:90, 2] = np.nan
obs.iloc[119:130, 0] = np.nan
obs.iloc[119:130, 2] = np.nan
mod = cls.create_model(obs, **kwargs)
cls.model = mod.ssm
n_disturbance_variates = (
(cls.model.k_endog + cls.model.k_posdef) * cls.model.nobs
)
np.random.seed(1234)
dv = np.random.normal(size=n_disturbance_variates)
isv = np.random.normal(size=cls.model.k_states)
# Collapsed filtering, smoothing, and simulation smoothing
cls.model.filter_collapsed = True
cls.results_b = cls.model.smooth()
cls.sim_b = cls.model.simulation_smoother()
cls.sim_b.simulate(disturbance_variates=dv, initial_state_variates=isv)
# Conventional filtering, smoothing, and simulation smoothing
cls.model.filter_collapsed = False
cls.results_a = cls.model.smooth()
cls.sim_a = cls.model.simulation_smoother()
cls.sim_a.simulate(disturbance_variates=dv, initial_state_variates=isv)
# Create the model with augmented state space
kwargs.pop('filter_collapsed', None)
mod = MLEModel(obs, k_states=4, k_posdef=2, **kwargs)
mod['design', :3, :2] = np.array([[-32.47143586, 17.33779024],
[-7.40264169, 1.69279859],
[-209.04702853, 125.2879374]])
mod['obs_cov'] = np.diag(
np.array([0.0622668, 1.95666886, 58.37473642]))
mod['transition', :2, :2] = np.array([[0.29935707, 0.33289005],
[-0.7639868, 1.2844237]])
mod['transition', 2:, :2] = np.eye(2)
mod['selection', :2, :2] = np.eye(2)
mod['state_cov'] = np.array([[1.2, -0.25],
[-0.25, 1.1]])
mod.initialize_approximate_diffuse(1e6)
cls.augmented_model = mod.ssm
cls.augmented_results = mod.ssm.smooth()
def setup_class(cls, which='none', **kwargs):
# Results
path = current_path + os.sep + 'results/results_smoothing_generalobscov_R.csv'
cls.desired = pd.read_csv(path)
# Data
dta = datasets.macrodata.load_pandas().data
dta.index = pd.date_range(start='1959-01-01', end='2009-7-01', freq='QS')
obs = dta[['realgdp','realcons','realinv']].diff().iloc[1:]
if which == 'all':
obs.iloc[:50, :] = np.nan
obs.iloc[119:130, :] = np.nan
elif which == 'partial':
obs.iloc[0:50, 0] = np.nan
obs.iloc[119:130, 0] = np.nan
elif which == 'mixed':
obs.iloc[0:50, 0] = np.nan
obs.iloc[19:70, 1] = np.nan
obs.iloc[39:90, 2] = np.nan
obs.iloc[119:130, 0] = np.nan
obs.iloc[119:130, 2] = np.nan
# Create the model
mod = MLEModel(obs, k_states=3, k_posdef=3, **kwargs)
mod['design'] = np.eye(3)
mod['obs_cov'] = np.array([[ 609.0746647855, 0. , 0. ],
[ 0. , 1.8774916622, 0. ],
[ 0. , 0. , 124.6768281675]])
mod['transition'] = np.array([[-0.8110473405, 1.8005304445, 1.0215975772],
[-1.9846632699, 2.4091302213, 1.9264449765],
[ 0.9181658823, -0.2442384581, -0.6393462272]])
mod['selection'] = np.eye(3)
mod['state_cov'] = np.array([[ 1552.9758843938, 612.7185121905, 877.6157204992],
[ 612.7185121905, 467.8739411204, 70.608037339 ],
[ 877.6157204992, 70.608037339 , 900.5440385836]])
mod.initialize_approximate_diffuse(1e6)
cls.model = mod.ssm
# Conventional filtering, smoothing, and simulation smoothing
cls.model.filter_conventional = True
cls.conventional_results = cls.model.smooth()
n_disturbance_variates = (
(cls.model.k_endog + cls.model.k_posdef) * cls.model.nobs
)
cls.conventional_sim = cls.model.simulation_smoother(
disturbance_variates=np.zeros(n_disturbance_variates),
initial_state_variates=np.zeros(cls.model.k_states)
)
# Univariate filtering, smoothing, and simulation smoothing
cls.model.filter_univariate = True
cls.univariate_results = cls.model.smooth()
cls.univariate_sim = cls.model.simulation_smoother(
disturbance_variates=np.zeros(n_disturbance_variates),
initial_state_variates=np.zeros(cls.model.k_states)
)
def setup_class(cls, which, dtype=float, alternate_timing=False, **kwargs):
# Results
path = os.path.join(current_path, 'results',
'results_smoothing_generalobscov_R.csv')
cls.desired = pd.read_csv(path)
# Data
dta = datasets.macrodata.load_pandas().data
dta.index = pd.date_range(start='1959-01-01',
end='2009-7-01', freq='QS')
obs = dta[['realgdp', 'realcons', 'realinv']].diff().iloc[1:]
if which == 'all':
obs.iloc[:50, :] = np.nan
obs.iloc[119:130, :] = np.nan
elif which == 'partial':
obs.iloc[0:50, 0] = np.nan
obs.iloc[119:130, 0] = np.nan
elif which == 'mixed':
obs.iloc[0:50, 0] = np.nan
obs.iloc[19:70, 1] = np.nan
obs.iloc[39:90, 2] = np.nan
obs.iloc[119:130, 0] = np.nan
obs.iloc[119:130, 2] = np.nan
# Create the model
mod = MLEModel(obs, k_states=3, k_posdef=3, **kwargs)
mod['design'] = np.eye(3)
X = (np.arange(9) + 1).reshape((3, 3)) / 10.
mod['obs_cov'] = np.dot(X, X.T)
mod['transition'] = np.eye(3)
mod['selection'] = np.eye(3)
mod['state_cov'] = np.eye(3)
mod.initialize_approximate_diffuse(1e6)
cls.model = mod.ssm
# Conventional filtering, smoothing, and simulation smoothing
cls.model.filter_conventional = True
cls.conventional_results = cls.model.smooth()
n_disturbance_variates = (
(cls.model.k_endog + cls.model.k_posdef) * cls.model.nobs
)
cls.conventional_sim = cls.model.simulation_smoother(
disturbance_variates=np.zeros(n_disturbance_variates),
initial_state_variates=np.zeros(cls.model.k_states)
)
# Univariate filtering, smoothing, and simulation smoothing
cls.model.filter_univariate = True
cls.univariate_results = cls.model.smooth()
cls.univariate_sim = cls.model.simulation_smoother(
disturbance_variates=np.zeros(n_disturbance_variates),
initial_state_variates=np.zeros(cls.model.k_states)
)
def test_diagnostics_nile_durbinkoopman():
# Test the diagnostic tests using the Nile dataset. Results are from
# Durbin and Koopman (2012); parameter values reported on page 37; test
# statistics on page 40
niledata = nile.data.load_pandas().data
niledata.index = pd.date_range('1871-01-01', '1970-01-01', freq='AS')
mod = MLEModel(niledata['volume'],
k_states=1,
initialization='approximate_diffuse',
initial_variance=1e15,
loglikelihood_burn=1)
mod.ssm['design', 0, 0] = 1
mod.ssm['obs_cov', 0, 0] = 15099.
mod.ssm['transition', 0, 0] = 1
mod.ssm['selection', 0, 0] = 1
mod.ssm['state_cov', 0, 0] = 1469.1
res = mod.filter([])
# Test Ljung-Box
# Note: only 3 digits provided in the reference paper
actual = res.test_serial_correlation(method='ljungbox', lags=9)[0, 0, -1]
assert_allclose(actual, [8.84], atol=1e-2)
# Test Jarque-Bera
# Note: The book reports 0.09 for Kurtosis, because it is reporting the
# statistic less the mean of the Kurtosis distribution (which is 3).
norm = res.test_normality(method='jarquebera')[0]
actual = [norm[0], norm[2], norm[3]]
assert_allclose(actual, [0.05, -0.03, 3.09], atol=1e-2)
# Test Heteroskedasticity
# Note: only 2 digits provided in the book
actual = res.test_heteroskedasticity(method='breakvar')[0, 0]
assert_allclose(actual, [0.61], atol=1e-2)
def test_diagnostics_nile_eviews():
# Test the diagnostic tests using the Nile dataset. Results are from
# "Fitting State Space Models with EViews" (Van den Bossche 2011,
# Journal of Statistical Software).
# For parameter values, see Figure 2
# For Ljung-Box and Jarque-Bera statistics and p-values, see Figure 5
# The Heteroskedasticity statistic is not provided in this paper.
niledata = nile.data.load_pandas().data
niledata.index = pd.date_range('1871-01-01', '1970-01-01', freq='AS')
mod = MLEModel(niledata['volume'],
k_states=1,
initialization='approximate_diffuse',
initial_variance=1e15,
loglikelihood_burn=1)
mod.ssm['design', 0, 0] = 1
mod.ssm['obs_cov', 0, 0] = np.exp(9.600350)
mod.ssm['transition', 0, 0] = 1
mod.ssm['selection', 0, 0] = 1
mod.ssm['state_cov', 0, 0] = np.exp(7.348705)
res = mod.filter([])
# Test Ljung-Box
# Note: only 3 digits provided in the reference paper
actual = res.test_serial_correlation(method='ljungbox', lags=10)[0, :, -1]
assert_allclose(actual, [13.117, 0.217], atol=1e-3)
# Test Jarque-Bera
actual = res.test_normality(method='jarquebera')[0, :2]
assert_allclose(actual, [0.041686, 0.979373], atol=1e-5)
def create_model(cls, obs, **kwargs):
# Create the model with typical state space
mod = MLEModel(obs, k_states=2, k_posdef=2, **kwargs)
mod['design'] = np.array([[-32.47143586, 17.33779024],
[-7.40264169, 1.69279859],
[-209.04702853, 125.2879374]])
mod['obs_cov'] = np.diag(np.array([0.0622668, 1.95666886,
58.37473642]))
mod['transition'] = np.array([[0.29935707, 0.33289005],
[-0.7639868, 1.2844237]])
mod['selection'] = np.eye(2)
mod['state_cov'] = np.array([[1.2, -0.25], [-0.25, 1.1]])
mod.initialize_approximate_diffuse(1e6)
return mod
def test_params():
mod = MLEModel([1,2], **kwargs)
# By default start_params raises NotImplementedError
assert_raises(NotImplementedError, lambda: mod.start_params)
# But param names are by default an empty array
assert_equal(mod.param_names, [])
# We can set them in the object if we want
mod._start_params = [1]
mod._param_names = ['a']
assert_equal(mod.start_params, [1])
assert_equal(mod.param_names, ['a'])
def test_params():
mod = MLEModel([1, 2], **kwargs)
# By default start_params raises NotImplementedError
assert_raises(NotImplementedError, lambda: mod.start_params)
# But param names are by default an empty array
assert_equal(mod.param_names, [])
# We can set them in the object if we want
mod._start_params = [1]
mod._param_names = ['a']
assert_equal(mod.start_params, [1])
assert_equal(mod.param_names, ['a'])
def test_forecast():
# Numpy
mod = MLEModel([1,2], **kwargs)
res = mod.filter([])
forecast = res.forecast(steps=10)
assert_allclose(forecast, np.ones((10,)) * 2)
assert_allclose(res.get_forecast(steps=10).predicted_mean, forecast)
# Pandas
index = pd.date_range('1960-01-01', periods=2, freq='MS')
mod = MLEModel(pd.Series([1,2], index=index), **kwargs)
res = mod.filter([])
assert_allclose(res.forecast(steps=10), np.ones((10,)) * 2)
assert_allclose(res.forecast(steps='1960-12-01'), np.ones((10,)) * 2)
assert_allclose(res.get_forecast(steps=10).predicted_mean, np.ones((10,)) * 2)
def test_summary():
dates = pd.date_range(start='1980-01-01', end='1984-01-01', freq='AS')
endog = pd.TimeSeries([1,2,3,4,5], index=dates)
mod = MLEModel(endog, **kwargs)
res = mod.filter([])
# Get the summary
txt = str(res.summary())
# Test res.summary when the model has dates
assert_equal(re.search('Sample:\s+01-01-1980', txt) is not None, True)
assert_equal(re.search('\s+- 01-01-1984', txt) is not None, True)
# Test res.summary when `model_name` was not provided
assert_equal(re.search('Model:\s+MLEModel', txt) is not None, True)
def test_summary():
dates = pd.date_range(start='1980-01-01', end='1984-01-01', freq='AS')
endog = pd.Series([1,2,3,4,5], index=dates)
mod = MLEModel(endog, **kwargs)
res = mod.filter([])
# Get the summary
txt = str(res.summary())
# Test res.summary when the model has dates
assert_equal(re.search('Sample:\s+01-01-1980', txt) is not None, True)
assert_equal(re.search('\s+- 01-01-1984', txt) is not None, True)
# Test res.summary when `model_name` was not provided
assert_equal(re.search('Model:\s+MLEModel', txt) is not None, True)
def create_model(cls, obs, **kwargs):
# Create the model with typical state space
mod = MLEModel(obs, k_states=2, k_posdef=2, **kwargs)
mod['design'] = np.array([[-32.47143586, 17.33779024],
[-7.40264169, 1.69279859],
[-209.04702853, 125.2879374]])
mod['obs_cov'] = np.diag(
np.array([0.0622668, 1.95666886, 58.37473642]))
mod['transition'] = np.array([[0.29935707, 0.33289005],
[-0.7639868, 1.2844237]])
mod['selection'] = np.eye(2)
mod['state_cov'] = np.array([[1.2, -0.25],
[-0.25, 1.1]])
mod.initialize_approximate_diffuse(1e6)
return mod
def test_init_matrices_time_invariant():
# Test setting state space system matrices in __init__, with time-invariant
# matrices
k_endog = 2
k_states = 3
k_posdef = 1
endog = np.zeros((10, 2))
obs_intercept = np.arange(k_endog) * 1.0
design = np.reshape(
np.arange(k_endog * k_states) * 1.0, (k_endog, k_states))
obs_cov = np.reshape(np.arange(k_endog**2) * 1.0, (k_endog, k_endog))
state_intercept = np.arange(k_states) * 1.0
transition = np.reshape(np.arange(k_states**2) * 1.0, (k_states, k_states))
selection = np.reshape(
np.arange(k_states * k_posdef) * 1.0, (k_states, k_posdef))
state_cov = np.reshape(np.arange(k_posdef**2) * 1.0, (k_posdef, k_posdef))
mod = MLEModel(endog, k_states=k_states, k_posdef=k_posdef,
obs_intercept=obs_intercept, design=design,
obs_cov=obs_cov, state_intercept=state_intercept,
transition=transition, selection=selection,
state_cov=state_cov)
assert_allclose(mod['obs_intercept'], obs_intercept)
assert_allclose(mod['design'], design)
assert_allclose(mod['obs_cov'], obs_cov)
assert_allclose(mod['state_intercept'], state_intercept)
assert_allclose(mod['transition'], transition)
assert_allclose(mod['selection'], selection)
assert_allclose(mod['state_cov'], state_cov)
def setup_class(cls, dtype=float, alternate_timing=False, **kwargs):
cls.results = results_kalman_filter.uc_bi
# GDP and Unemployment, Quarterly, 1948.1 - 1995.3
data = pd.DataFrame(
cls.results['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)
data['X'] = np.exp(data['GDP']) * data['UNEMP']
k_states = 2
cls.mlemodel = MLEModel(data, k_states=k_states, **kwargs)
cls.model = cls.mlemodel.ssm
if alternate_timing:
cls.model.timing_init_filtered = True
# Statespace representation
cls.model['selection'] = np.eye(cls.model.k_states)
# Update matrices with test parameters
cls.model['design'] = np.array([[0.5, 0.2],
[0, 0.8],
[1, -0.5]])
cls.model['transition'] = np.array([[0.4, 0.5],
[1, 0]])
cls.model['obs_cov'] = np.diag([0.2, 1.1, 0.5])
cls.model['state_cov'] = np.diag([2., 1])
# Initialization
cls.model.initialize_approximate_diffuse()
def check_stationary_initialization_2dim(dtype=float):
endog = np.zeros(10, dtype=dtype)
# 2-dimensional example
mod = MLEModel(endog, k_states=2, k_posdef=2)
mod.ssm.initialize_stationary()
intercept = np.array([2.3, -10.2], dtype=dtype)
phi = np.array([[0.8, 0.1], [-0.2, 0.7]], dtype=dtype)
sigma2 = np.array([[1.4, -0.2], [-0.2, 4.5]], dtype=dtype)
mod['state_intercept'] = intercept
mod['transition'] = phi
mod['selection'] = np.eye(2).astype(dtype)
mod['state_cov'] = sigma2
mod.ssm._initialize_filter()
mod.ssm._initialize_state()
_statespace = mod.ssm._statespace
initial_state = np.array(_statespace.initial_state)
initial_state_cov = np.array(_statespace.initial_state_cov)
desired = np.linalg.solve(np.eye(2).astype(dtype) - phi, intercept)
assert_allclose(initial_state, desired)
desired = solve_discrete_lyapunov(phi, sigma2)
# precision reductions only required for single precision float / complex
assert_allclose(initial_state_cov, desired, atol=1e-5)
def check_stationary_initialization_1dim(dtype=float):
endog = np.zeros(10, dtype=dtype)
# 1-dimensional example
mod = MLEModel(endog, k_states=1, k_posdef=1)
mod.ssm.initialize_stationary()
intercept = np.array([2.3], dtype=dtype)
phi = np.diag([0.9]).astype(dtype)
sigma2 = np.diag([1.3]).astype(dtype)
mod['state_intercept'] = intercept
mod['transition'] = phi
mod['selection'] = np.eye(1).astype(dtype)
mod['state_cov'] = sigma2
mod.ssm._initialize_filter()
mod.ssm._initialize_state()
_statespace = mod.ssm._statespace
initial_state = np.array(_statespace.initial_state)
initial_state_cov = np.array(_statespace.initial_state_cov)
# precision reductions only required for float complex case
# mean = intercept + phi * mean
# intercept = (1 - phi) * mean
# mean = intercept / (1 - phi)
assert_allclose(initial_state, intercept / (1 - phi[0, 0]))
desired = np.linalg.inv(np.eye(1) - phi).dot(intercept)
assert_allclose(initial_state, desired)
# var = phi**2 var + sigma2
# var = sigma2 / (1 - phi**2)
assert_allclose(initial_state_cov, sigma2 / (1 - phi**2))
assert_allclose(initial_state_cov, solve_discrete_lyapunov(phi, sigma2))
def test_filter():
endog = np.array([1., 2.])
mod = MLEModel(endog, **kwargs)
# Test return of ssm object
res = mod.filter([], return_ssm=True)
assert_equal(isinstance(res, kalman_filter.FilterResults), True)
# Test return of full results object
res = mod.filter([])
assert_equal(isinstance(res, MLEResultsWrapper), True)
assert_equal(res.cov_type, 'opg')
# Test return of full results object, specific covariance type
res = mod.filter([], cov_type='oim')
assert_equal(isinstance(res, MLEResultsWrapper), True)
assert_equal(res.cov_type, 'oim')
def test_filter():
endog = np.array([1., 2.])
mod = MLEModel(endog, **kwargs)
# Test return of ssm object
res = mod.filter([], return_ssm=True)
assert_equal(isinstance(res, kalman_filter.FilterResults), True)
# Test return of full results object
res = mod.filter([])
assert_equal(isinstance(res, MLEResultsWrapper), True)
assert_equal(res.cov_type, 'opg')
# Test return of full results object, specific covariance type
res = mod.filter([], cov_type='oim')
assert_equal(isinstance(res, MLEResultsWrapper), True)
assert_equal(res.cov_type, 'oim')
def test_diagnostics_nile_durbinkoopman():
# Test the diagnostic tests using the Nile dataset. Results are from
# Durbin and Koopman (2012); parameter values reported on page 37; test
# statistics on page 40
niledata = nile.data.load_pandas().data
niledata.index = pd.date_range('1871-01-01', '1970-01-01', freq='AS')
mod = MLEModel(niledata['volume'], k_states=1,
initialization='approximate_diffuse', initial_variance=1e15,
loglikelihood_burn=1)
mod.ssm['design', 0, 0] = 1
mod.ssm['obs_cov', 0, 0] = 15099.
mod.ssm['transition', 0, 0] = 1
mod.ssm['selection', 0, 0] = 1
mod.ssm['state_cov', 0, 0] = 1469.1
res = mod.filter([])
# Test Ljung-Box
# Note: only 3 digits provided in the reference paper
actual = res.test_serial_correlation(method='ljungbox', lags=9)[0, 0, -1]
assert_allclose(actual, [8.84], atol=1e-2)
# Test Jarque-Bera
# Note: The book reports 0.09 for Kurtosis, because it is reporting the
# statistic less the mean of the Kurtosis distribution (which is 3).
norm = res.test_normality(method='jarquebera')[0]
actual = [norm[0], norm[2], norm[3]]
assert_allclose(actual, [0.05, -0.03, 3.09], atol=1e-2)
# Test Heteroskedasticity
# Note: only 2 digits provided in the book
actual = res.test_heteroskedasticity(method='breakvar')[0, 0]
assert_allclose(actual, [0.61], atol=1e-2)
def test_diagnostics_nile_eviews():
# Test the diagnostic tests using the Nile dataset. Results are from
# "Fitting State Space Models with EViews" (Van den Bossche 2011,
# Journal of Statistical Software).
# For parameter values, see Figure 2
# For Ljung-Box and Jarque-Bera statistics and p-values, see Figure 5
# The Heteroskedasticity statistic is not provided in this paper.
niledata = nile.data.load_pandas().data
niledata.index = pd.date_range('1871-01-01', '1970-01-01', freq='AS')
mod = MLEModel(niledata['volume'], k_states=1,
initialization='approximate_diffuse', initial_variance=1e15,
loglikelihood_burn=1)
mod.ssm['design', 0, 0] = 1
mod.ssm['obs_cov', 0, 0] = np.exp(9.600350)
mod.ssm['transition', 0, 0] = 1
mod.ssm['selection', 0, 0] = 1
mod.ssm['state_cov', 0, 0] = np.exp(7.348705)
res = mod.filter([])
# Test Ljung-Box
# Note: only 3 digits provided in the reference paper
actual = res.test_serial_correlation(method='ljungbox', lags=10)[0, :, -1]
assert_allclose(actual, [13.117, 0.217], atol=1e-3)
# Test Jarque-Bera
actual = res.test_normality(method='jarquebera')[0, :2]
assert_allclose(actual, [0.041686, 0.979373], atol=1e-5)
def test_predict():
dates = pd.date_range(start='1980-01-01', end='1981-01-01', freq='AS')
endog = pd.TimeSeries([1,2], index=dates)
mod = MLEModel(endog, **kwargs)
res = mod.filter([])
# Test that predict with start=None, end=None does prediction with full
# dataset
assert_equal(res.predict().shape, (mod.k_endog, mod.nobs))
# Test a string value to the dynamic option
assert_allclose(res.predict(dynamic='1981-01-01'), res.predict())
# Test an invalid date string value to the dynamic option
assert_raises(ValueError, res.predict, dynamic='1982-01-01')
# Test predict with full results
assert_equal(isinstance(res.predict(full_results=True),
kalman_filter.FilterResults), True)
def test_predict():
dates = pd.date_range(start='1980-01-01', end='1981-01-01', freq='AS')
endog = pd.TimeSeries([1, 2], index=dates)
mod = MLEModel(endog, **kwargs)
res = mod.filter([])
# Test that predict with start=None, end=None does prediction with full
# dataset
assert_equal(res.predict().shape, (mod.k_endog, mod.nobs))
# Test a string value to the dynamic option
assert_allclose(res.predict(dynamic='1981-01-01'), res.predict())
# Test an invalid date string value to the dynamic option
assert_raises(ValueError, res.predict, dynamic='1982-01-01')
# Test predict with full results
assert_equal(
isinstance(res.predict(full_results=True),
kalman_filter.FilterResults), True)
def test_predict():
dates = pd.date_range(start='1980-01-01', end='1981-01-01', freq='AS')
endog = pd.TimeSeries([1,2], index=dates)
mod = MLEModel(endog, **kwargs)
res = mod.filter([])
# Test that predict with start=None, end=None does prediction with full
# dataset
predict = res.predict()
assert_equal(predict.shape, (mod.nobs,))
assert_allclose(res.get_prediction().predicted_mean, predict)
# Test a string value to the dynamic option
assert_allclose(res.predict(dynamic='1981-01-01'), res.predict())
# Test an invalid date string value to the dynamic option
assert_raises(ValueError, res.predict, dynamic='1982-01-01')
# Test for passing a string to predict when dates are not set
mod = MLEModel([1,2], **kwargs)
res = mod.filter([])
assert_raises(ValueError, res.predict, dynamic='string')
def test_summary():
dates = pd.date_range(start='1980-01-01', end='1984-01-01', freq='AS')
endog = pd.Series([1,2,3,4,5], index=dates)
mod = MLEModel(endog, **kwargs)
res = mod.filter([])
# Get the summary
txt = str(res.summary())
# Test res.summary when the model has dates
assert_equal(re.search('Sample:\s+01-01-1980', txt) is not None, True)
assert_equal(re.search('\s+- 01-01-1984', txt) is not None, True)
# Test res.summary when `model_name` was not provided
assert_equal(re.search('Model:\s+MLEModel', txt) is not None, True)
# Smoke test that summary still works when diagnostic tests fail
res.filter_results._standardized_forecasts_error[:] = np.nan
res.summary()
res.filter_results._standardized_forecasts_error = 1
res.summary()
res.filter_results._standardized_forecasts_error = 'a'
res.summary()
def check_endog(endog, nobs=2, k_endog=1, **kwargs):
# create the model
mod = MLEModel(endog, **kwargs)
# the data directly available in the model is the Statsmodels version of
# the data; it should be 2-dim, C-contiguous, long-shaped:
# (nobs, k_endog) == (2, 1)
assert_equal(mod.endog.ndim, 2)
assert_equal(mod.endog.flags['C_CONTIGUOUS'], True)
assert_equal(mod.endog.shape, (nobs, k_endog))
# the data in the `ssm` object is the state space version of the data; it
# should be 2-dim, F-contiguous, wide-shaped (k_endog, nobs) == (1, 2)
# and it should share data with mod.endog
assert_equal(mod.ssm.endog.ndim, 2)
assert_equal(mod.ssm.endog.flags['F_CONTIGUOUS'], True)
assert_equal(mod.ssm.endog.shape, (k_endog, nobs))
assert_equal(mod.ssm.endog.base is mod.endog, True)
return mod
def test_forecast():
# Numpy
mod = MLEModel([1,2], **kwargs)
res = mod.filter([])
forecast = res.forecast(steps=10)
assert_allclose(forecast, np.ones((10,)) * 2)
assert_allclose(res.get_forecast(steps=10).predicted_mean, forecast)
# Pandas
index = pd.date_range('1960-01-01', periods=2, freq='MS')
mod = MLEModel(pd.Series([1,2], index=index), **kwargs)
res = mod.filter([])
assert_allclose(res.forecast(steps=10), np.ones((10,)) * 2)
assert_allclose(res.forecast(steps='1960-12-01'), np.ones((10,)) * 2)
assert_allclose(res.get_forecast(steps=10).predicted_mean, np.ones((10,)) * 2)
def test_predict():
dates = pd.date_range(start='1980-01-01', end='1981-01-01', freq='AS')
endog = pd.Series([1, 2], index=dates)
mod = MLEModel(endog, **kwargs)
res = mod.filter([])
# Test that predict with start=None, end=None does prediction with full
# dataset
predict = res.predict()
assert_equal(predict.shape, (mod.nobs, ))
assert_allclose(res.get_prediction().predicted_mean, predict)
# Test a string value to the dynamic option
assert_allclose(res.predict(dynamic='1981-01-01'), res.predict())
# Test an invalid date string value to the dynamic option
# assert_raises(ValueError, res.predict, dynamic='1982-01-01')
# Test for passing a string to predict when dates are not set
mod = MLEModel([1, 2], **kwargs)
res = mod.filter([])
assert_raises(KeyError, res.predict, dynamic='string')
def test_transform():
# The transforms in MLEModel are noops
mod = MLEModel([1, 2], **kwargs)
# Test direct transform, untransform
assert_allclose(mod.transform_params([2, 3]), [2, 3])
assert_allclose(mod.untransform_params([2, 3]), [2, 3])
# Smoke test for transformation in `filter`, `update`, `loglike`,
# `loglikeobs`
mod.filter([], transformed=False)
mod.update([], transformed=False)
mod.loglike([], transformed=False)
mod.loglikeobs([], transformed=False)
# Note that mod is an SARIMAX instance, and the two parameters are
# variances
mod, _ = get_dummy_mod(fit=False)
# Test direct transform, untransform
assert_allclose(mod.transform_params([2, 3]), [4, 9])
assert_allclose(mod.untransform_params([4, 9]), [2, 3])
# Test transformation in `filter`
res = mod.filter([2, 3], transformed=True)
assert_allclose(res.params, [2, 3])
res = mod.filter([2, 3], transformed=False)
assert_allclose(res.params, [4, 9])
def test_from_formula():
assert_raises(NotImplementedError, lambda: MLEModel.from_formula(1, 2, 3))
def test_from_formula():
assert_raises(NotImplementedError, lambda: MLEModel.from_formula(1,2,3))
def test_transform():
# The transforms in MLEModel are noops
mod = MLEModel([1,2], **kwargs)
# Test direct transform, untransform
assert_allclose(mod.transform_params([2, 3]), [2, 3])
assert_allclose(mod.untransform_params([2, 3]), [2, 3])
# Smoke test for transformation in `filter`, `update`, `loglike`,
# `loglikeobs`
mod.filter([], transformed=False)
mod.update([], transformed=False)
mod.loglike([], transformed=False)
mod.loglikeobs([], transformed=False)
# Note that mod is an SARIMAX instance, and the two parameters are
# variances
mod, _ = get_dummy_mod(fit=False)
# Test direct transform, untransform
assert_allclose(mod.transform_params([2, 3]), [4, 9])
assert_allclose(mod.untransform_params([4, 9]), [2, 3])
# Test transformation in `filter`
res = mod.filter([2, 3], transformed=True)
assert_allclose(res.params, [2, 3])
res = mod.filter([2, 3], transformed=False)
assert_allclose(res.params, [4, 9])
def test_basic_endog():
# Test various types of basic python endog inputs (e.g. lists, scalars...)
# Check cannot call with non-array_like
# fails due to checks in Statsmodels base classes
assert_raises(ValueError, MLEModel, endog=1, k_states=1)
assert_raises(ValueError, MLEModel, endog='a', k_states=1)
assert_raises(ValueError, MLEModel, endog=True, k_states=1)
# Check behavior with different types
mod = MLEModel([1], **kwargs)
res = mod.filter([])
assert_equal(res.filter_results.endog, [[1]])
mod = MLEModel([1.], **kwargs)
res = mod.filter([])
assert_equal(res.filter_results.endog, [[1]])
mod = MLEModel([True], **kwargs)
res = mod.filter([])
assert_equal(res.filter_results.endog, [[1]])
mod = MLEModel(['a'], **kwargs)
# raises error due to inability coerce string to numeric
assert_raises(ValueError, mod.filter, [])
# Check that a different iterable tpyes give the expected result
endog = [1., 2.]
mod = check_endog(endog, **kwargs)
mod.filter([])
endog = [[1.], [2.]]
mod = check_endog(endog, **kwargs)
mod.filter([])
endog = (1., 2.)
mod = check_endog(endog, **kwargs)
mod.filter([])
def test_basic_endog():
# Test various types of basic python endog inputs (e.g. lists, scalars...)
# Check cannot call with non-array-like
# fails due to checks in Statsmodels base classes
assert_raises(ValueError, MLEModel, endog=1, k_states=1)
assert_raises(ValueError, MLEModel, endog='a', k_states=1)
assert_raises(ValueError, MLEModel, endog=True, k_states=1)
# Check behavior with different types
mod = MLEModel([1], **kwargs)
res = mod.filter([])
assert_equal(res.filter_results.endog, [[1]])
mod = MLEModel([1.], **kwargs)
res = mod.filter([])
assert_equal(res.filter_results.endog, [[1]])
mod = MLEModel([True], **kwargs)
res = mod.filter([])
assert_equal(res.filter_results.endog, [[1]])
mod = MLEModel(['a'], **kwargs)
# raises error due to inability coerce string to numeric
assert_raises(ValueError, mod.filter, [])
# Check that a different iterable tpyes give the expected result
endog = [1.,2.]
mod = check_endog(endog, **kwargs)
mod.filter([])
endog = [[1.],[2.]]
mod = check_endog(endog, **kwargs)
mod.filter([])
endog = (1.,2.)
mod = check_endog(endog, **kwargs)
mod.filter([])
def setup_class(cls, which='none', **kwargs):
# Results
path = os.path.join(current_path, 'results',
'results_smoothing_generalobscov_R.csv')
cls.desired = pd.read_csv(path)
# Data
dta = datasets.macrodata.load_pandas().data
dta.index = pd.date_range(start='1959-01-01',
end='2009-7-01', freq='QS')
obs = dta[['realgdp', 'realcons', 'realinv']].diff().iloc[1:]
if which == 'all':
obs.iloc[:50, :] = np.nan
obs.iloc[119:130, :] = np.nan
elif which == 'partial':
obs.iloc[0:50, 0] = np.nan
obs.iloc[119:130, 0] = np.nan
elif which == 'mixed':
obs.iloc[0:50, 0] = np.nan
obs.iloc[19:70, 1] = np.nan
obs.iloc[39:90, 2] = np.nan
obs.iloc[119:130, 0] = np.nan
obs.iloc[119:130, 2] = np.nan
# Create the model
mod = MLEModel(obs, k_states=3, k_posdef=3, **kwargs)
mod['design'] = np.eye(3)
mod['obs_cov'] = np.array([
[609.0746647855, 0., 0.],
[0., 1.8774916622, 0.],
[0., 0., 124.6768281675]])
mod['transition'] = np.array([
[-0.8110473405, 1.8005304445, 1.0215975772],
[-1.9846632699, 2.4091302213, 1.9264449765],
[0.9181658823, -0.2442384581, -0.6393462272]])
mod['selection'] = np.eye(3)
mod['state_cov'] = np.array([
[1552.9758843938, 612.7185121905, 877.6157204992],
[612.7185121905, 467.8739411204, 70.608037339],
[877.6157204992, 70.608037339, 900.5440385836]])
mod.initialize_approximate_diffuse(1e6)
cls.model = mod.ssm
# Conventional filtering, smoothing, and simulation smoothing
cls.model.filter_conventional = True
cls.conventional_results = cls.model.smooth()
n_disturbance_variates = (
(cls.model.k_endog + cls.model.k_posdef) * cls.model.nobs
)
cls.conventional_sim = cls.model.simulation_smoother(
disturbance_variates=np.zeros(n_disturbance_variates),
initial_state_variates=np.zeros(cls.model.k_states)
)
# Univariate filtering, smoothing, and simulation smoothing
cls.model.filter_univariate = True
cls.univariate_results = cls.model.smooth()
cls.univariate_sim = cls.model.simulation_smoother(
disturbance_variates=np.zeros(n_disturbance_variates),
initial_state_variates=np.zeros(cls.model.k_states)
)
def setup_class(cls, dtype=float, alternate_timing=False, **kwargs):
cls.true = results_kalman_filter.uc_bi
cls.true_states = pd.DataFrame(cls.true['states'])
# GDP and Unemployment, Quarterly, 1948.1 - 1995.3
data = pd.DataFrame(cls.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
cls.mlemodel = MLEModel(data, k_states=k_states, **kwargs)
cls.model = cls.mlemodel.ssm
# Statespace representation
cls.model.design[:, :, 0] = [[1, 1, 0, 0, 0, 0], [0, 0, 0, 0, 0, 1]]
cls.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]
cls.model.selection = np.eye(cls.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(cls.true['parameters'], )
cls.model.design[([1, 1, 1], [1, 2,
3], [0, 0,
0])] = [alpha_1, alpha_2, alpha_3]
cls.model.transition[([1, 1], [1, 2], [0, 0])] = [phi_1, phi_2]
cls.model.obs_cov[1, 1, 0] = sigma_ec**2
cls.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: cls.modification
if not alternate_timing:
initial_state_cov = np.dot(
np.dot(cls.model.transition[:, :, 0], initial_state_cov),
cls.model.transition[:, :, 0].T)
else:
cls.model.timing_init_filtered = True
cls.model.initialize_known(initial_state, initial_state_cov)
# Conventional filtering, smoothing, and simulation smoothing
cls.model.filter_conventional = True
cls.conventional_results = cls.model.smooth()
n_disturbance_variates = ((cls.model.k_endog + cls.model.k_posdef) *
cls.model.nobs)
cls.conventional_sim = cls.model.simulation_smoother(
disturbance_variates=np.zeros(n_disturbance_variates),
initial_state_variates=np.zeros(cls.model.k_states))
# Univariate filtering, smoothing, and simulation smoothing
cls.model.filter_univariate = True
cls.univariate_results = cls.model.smooth()
cls.univariate_sim = cls.model.simulation_smoother(
disturbance_variates=np.zeros(n_disturbance_variates),
initial_state_variates=np.zeros(cls.model.k_states))
def test_forecast():
mod = MLEModel([1,2], **kwargs)
res = mod.filter([])
assert_allclose(res.forecast(steps=10), [[2]*10])
def test_numpy_endog():
# Test various types of numpy endog inputs
# Check behavior of the link maintained between passed `endog` and
# `mod.endog` arrays
endog = np.array([1., 2.])
mod = MLEModel(endog, **kwargs)
assert_equal(mod.endog.base is not mod.data.orig_endog, True)
assert_equal(mod.endog.base is not endog, True)
assert_equal(mod.data.orig_endog.base is not endog, True)
endog[0] = 2
# there is no link to mod.endog
assert_equal(mod.endog, np.r_[1, 2].reshape(2,1))
# there remains a link to mod.data.orig_endog
assert_equal(mod.data.orig_endog, endog)
# Check behavior with different memory layouts / shapes
# Example (failure): 0-dim array
endog = np.array(1.)
# raises error due to len(endog) failing in Statsmodels base classes
assert_raises(TypeError, check_endog, endog, **kwargs)
# Example : 1-dim array, both C- and F-contiguous, length 2
endog = np.array([1.,2.])
assert_equal(endog.ndim, 1)
assert_equal(endog.flags['C_CONTIGUOUS'], True)
assert_equal(endog.flags['F_CONTIGUOUS'], True)
assert_equal(endog.shape, (2,))
mod = check_endog(endog, **kwargs)
mod.filter([])
# Example : 2-dim array, C-contiguous, long-shaped: (nobs, k_endog)
endog = np.array([1., 2.]).reshape(2, 1)
assert_equal(endog.ndim, 2)
assert_equal(endog.flags['C_CONTIGUOUS'], True)
assert_equal(endog.flags['F_CONTIGUOUS'], False)
assert_equal(endog.shape, (2, 1))
mod = check_endog(endog, **kwargs)
mod.filter([])
# Example : 2-dim array, C-contiguous, wide-shaped: (k_endog, nobs)
endog = np.array([1., 2.]).reshape(1, 2)
assert_equal(endog.ndim, 2)
assert_equal(endog.flags['C_CONTIGUOUS'], True)
assert_equal(endog.flags['F_CONTIGUOUS'], False)
assert_equal(endog.shape, (1, 2))
# raises error because arrays are always interpreted as
# (nobs, k_endog), which means that k_endog=2 is incompatibile with shape
# of design matrix (1, 1)
assert_raises(ValueError, check_endog, endog, **kwargs)
# Example : 2-dim array, F-contiguous, long-shaped (nobs, k_endog)
endog = np.array([1., 2.]).reshape(1, 2).transpose()
assert_equal(endog.ndim, 2)
assert_equal(endog.flags['C_CONTIGUOUS'], False)
assert_equal(endog.flags['F_CONTIGUOUS'], True)
assert_equal(endog.shape, (2, 1))
mod = check_endog(endog, **kwargs)
mod.filter([])
# Example : 2-dim array, F-contiguous, wide-shaped (k_endog, nobs)
endog = np.array([1., 2.]).reshape(2, 1).transpose()
assert_equal(endog.ndim, 2)
assert_equal(endog.flags['C_CONTIGUOUS'], False)
assert_equal(endog.flags['F_CONTIGUOUS'], True)
assert_equal(endog.shape, (1, 2))
# raises error because arrays are always interpreted as
# (nobs, k_endog), which means that k_endog=2 is incompatibile with shape
# of design matrix (1, 1)
assert_raises(ValueError, check_endog, endog, **kwargs)
# Example (failure): 3-dim array
endog = np.array([1., 2.]).reshape(2, 1, 1)
# raises error due to direct ndim check in Statsmodels base classes
assert_raises(ValueError, check_endog, endog, **kwargs)
# Example : np.array with 2 columns
# Update kwargs for k_endog=2
kwargs2 = {
'k_states': 1, 'design': [[1], [0.]], 'obs_cov': [[1, 0], [0, 1]],
'transition': [[1]], 'selection': [[1]], 'state_cov': [[1]],
'initialization': 'approximate_diffuse'
}
endog = np.array([[1., 2.], [3., 4.]])
mod = check_endog(endog, k_endog=2, **kwargs2)
mod.filter([])
def test_forecast():
mod = MLEModel([1, 2], **kwargs)
res = mod.filter([])
assert_allclose(res.forecast(steps=10), [[2] * 10])
def test_numpy_endog():
# Test various types of numpy endog inputs
# Check behavior of the link maintained between passed `endog` and
# `mod.endog` arrays
endog = np.array([1., 2.])
mod = MLEModel(endog, **kwargs)
assert_equal(mod.endog.base is not mod.data.orig_endog, True)
assert_equal(mod.endog.base is not endog, True)
assert_equal(mod.data.orig_endog.base is not endog, True)
endog[0] = 2
# there is no link to mod.endog
assert_equal(mod.endog, np.r_[1, 2].reshape(2, 1))
# there remains a link to mod.data.orig_endog
assert_equal(mod.data.orig_endog, endog)
# Check behavior with different memory layouts / shapes
# Example (failure): 0-dim array
endog = np.array(1.)
# raises error due to len(endog) failing in Statsmodels base classes
assert_raises(TypeError, check_endog, endog, **kwargs)
# Example : 1-dim array, both C- and F-contiguous, length 2
endog = np.array([1., 2.])
assert_equal(endog.ndim, 1)
assert_equal(endog.flags['C_CONTIGUOUS'], True)
assert_equal(endog.flags['F_CONTIGUOUS'], True)
assert_equal(endog.shape, (2, ))
mod = check_endog(endog, **kwargs)
mod.filter([])
# Example : 2-dim array, C-contiguous, long-shaped: (nobs, k_endog)
endog = np.array([1., 2.]).reshape(2, 1)
assert_equal(endog.ndim, 2)
assert_equal(endog.flags['C_CONTIGUOUS'], True)
# On newer numpy (>= 0.10), this array is (rightly) both C and F contiguous
# assert_equal(endog.flags['F_CONTIGUOUS'], False)
assert_equal(endog.shape, (2, 1))
mod = check_endog(endog, **kwargs)
mod.filter([])
# Example : 2-dim array, C-contiguous, wide-shaped: (k_endog, nobs)
endog = np.array([1., 2.]).reshape(1, 2)
assert_equal(endog.ndim, 2)
assert_equal(endog.flags['C_CONTIGUOUS'], True)
# On newer numpy (>= 0.10), this array is (rightly) both C and F contiguous
# assert_equal(endog.flags['F_CONTIGUOUS'], False)
assert_equal(endog.shape, (1, 2))
# raises error because arrays are always interpreted as
# (nobs, k_endog), which means that k_endog=2 is incompatibile with shape
# of design matrix (1, 1)
assert_raises(ValueError, check_endog, endog, **kwargs)
# Example : 2-dim array, F-contiguous, long-shaped (nobs, k_endog)
endog = np.array([1., 2.]).reshape(1, 2).transpose()
assert_equal(endog.ndim, 2)
# On newer numpy (>= 0.10), this array is (rightly) both C and F contiguous
# assert_equal(endog.flags['C_CONTIGUOUS'], False)
assert_equal(endog.flags['F_CONTIGUOUS'], True)
assert_equal(endog.shape, (2, 1))
mod = check_endog(endog, **kwargs)
mod.filter([])
# Example : 2-dim array, F-contiguous, wide-shaped (k_endog, nobs)
endog = np.array([1., 2.]).reshape(2, 1).transpose()
assert_equal(endog.ndim, 2)
# On newer numpy (>= 0.10), this array is (rightly) both C and F contiguous
# assert_equal(endog.flags['C_CONTIGUOUS'], False)
assert_equal(endog.flags['F_CONTIGUOUS'], True)
assert_equal(endog.shape, (1, 2))
# raises error because arrays are always interpreted as
# (nobs, k_endog), which means that k_endog=2 is incompatibile with shape
# of design matrix (1, 1)
assert_raises(ValueError, check_endog, endog, **kwargs)
# Example (failure): 3-dim array
endog = np.array([1., 2.]).reshape(2, 1, 1)
# raises error due to direct ndim check in Statsmodels base classes
assert_raises(ValueError, check_endog, endog, **kwargs)
# Example : np.array with 2 columns
# Update kwargs for k_endog=2
kwargs2 = {
'k_states': 1,
'design': [[1], [0.]],
'obs_cov': [[1, 0], [0, 1]],
'transition': [[1]],
'selection': [[1]],
'state_cov': [[1]],
'initialization': 'approximate_diffuse'
}
endog = np.array([[1., 2.], [3., 4.]])
mod = check_endog(endog, k_endog=2, **kwargs2)
mod.filter([])
def setup_class(cls, which='mixed', *args, **kwargs):
# Data
dta = datasets.macrodata.load_pandas().data
dta.index = pd.date_range(start='1959-01-01', end='2009-7-01', freq='QS')
obs = np.log(dta[['realgdp','realcons','realinv']]).diff().iloc[1:] * 400
if which == 'all':
obs.iloc[:50, :] = np.nan
obs.iloc[119:130, :] = np.nan
elif which == 'partial':
obs.iloc[0:50, 0] = np.nan
obs.iloc[119:130, 0] = np.nan
elif which == 'mixed':
obs.iloc[0:50, 0] = np.nan
obs.iloc[19:70, 1] = np.nan
obs.iloc[39:90, 2] = np.nan
obs.iloc[119:130, 0] = np.nan
obs.iloc[119:130, 2] = np.nan
# Create the model with typical state space
mod = MLEModel(obs, k_states=2, k_posdef=2, **kwargs)
mod['design'] = np.array([[-32.47143586, 17.33779024],
[-7.40264169, 1.69279859],
[-209.04702853, 125.2879374]])
mod['obs_cov'] = np.diag(
np.array([0.0622668, 1.95666886, 58.37473642]))
mod['transition'] = np.array([[0.29935707, 0.33289005],
[-0.7639868, 1.2844237]])
mod['selection'] = np.eye(2)
mod['state_cov'] = np.array([[1.2, -0.25],
[-0.25, 1.1]])
mod.initialize_approximate_diffuse(1e6)
cls.model = mod.ssm
n_disturbance_variates = (
(cls.model.k_endog + cls.model.k_posdef) * cls.model.nobs
)
# Collapsed filtering, smoothing, and simulation smoothing
cls.model.filter_collapsed = True
cls.results_b = cls.model.smooth()
cls.sim_b = cls.model.simulation_smoother(
disturbance_variates=np.zeros(n_disturbance_variates),
initial_state_variates=np.zeros(cls.model.k_states)
)
# Conventional filtering, smoothing, and simulation smoothing
cls.model.filter_collapsed = False
cls.results_a = cls.model.smooth()
cls.sim_a = cls.model.simulation_smoother(
disturbance_variates=np.zeros(n_disturbance_variates),
initial_state_variates=np.zeros(cls.model.k_states)
)
# Create the model with augmented state space
kwargs.pop('filter_collapsed', None)
mod = MLEModel(obs, k_states=4, k_posdef=2, **kwargs)
mod['design', :3, :2] = np.array([[-32.47143586, 17.33779024],
[-7.40264169, 1.69279859],
[-209.04702853, 125.2879374]])
mod['obs_cov'] = np.diag(
np.array([0.0622668, 1.95666886, 58.37473642]))
mod['transition', :2, :2] = np.array([[0.29935707, 0.33289005],
[-0.7639868, 1.2844237]])
mod['transition', 2:, :2] = np.eye(2)
mod['selection', :2, :2] = np.eye(2)
mod['state_cov'] = np.array([[1.2, -0.25],
[-0.25, 1.1]])
mod.initialize_approximate_diffuse(1e6)
cls.augmented_model = mod.ssm
cls.augmented_results = mod.ssm.smooth()
