def test_irrelevant_state():
# This test records a case in which exact diffuse initialization leads to
# numerical problems, becuase the existence of an irrelevant state
# initialized as diffuse means that there is never a transition to the
# usual Kalman filter.
endog = macrodata.infl
spec = {
'freq_seasonal': [{'period':8, 'harmonics': 6},
{'period': 36, 'harmonics': 6}]
}
# Approximate diffuse version
mod = UnobservedComponents(endog, 'llevel', **spec)
mod.ssm.initialization = Initialization(mod.k_states,'approximate_diffuse')
res = mod.smooth([3.4, 7.2, 0.01, 0.01])
# Exact diffuse version
mod2 = UnobservedComponents(endog, 'llevel', **spec)
mod2.ssm.filter_univariate = True
mod2.ssm.initialization = Initialization(mod2.k_states, 'diffuse')
res2 = mod2.smooth([3.4, 7.2, 0.01, 0.01])
# Check that e.g. the filtered state for the level is equal
assert_allclose(res.filtered_state[0, 25:],
res2.filtered_state[0, 25:], atol=1e-5)
def test_forecast():
endog = np.arange(50) + 10
exog = np.arange(50)
mod = UnobservedComponents(endog, exog=exog, level='dconstant')
res = mod.smooth([1e-15, 1])
actual = res.forecast(10, exog=np.arange(50,60)[:,np.newaxis])
desired = np.arange(50,60) + 10
assert_allclose(actual, desired)
def test_forecast():
endog = np.arange(50) + 10
exog = np.arange(50)
mod = UnobservedComponents(endog, exog=exog, level='dconstant')
res = mod.smooth([1e-15, 1])
actual = res.forecast(10, exog=np.arange(50, 60)[:, np.newaxis])
desired = np.arange(50, 60) + 10
assert_allclose(actual, desired)
def test_apply_results():
endog = np.arange(100)
exog = np.ones_like(endog)
params = [1., 1., 0.1, 1.]
mod1 = UnobservedComponents(endog[:50], 'llevel', exog=exog[:50])
res1 = mod1.smooth(params)
mod2 = UnobservedComponents(endog[50:], 'llevel', exog=exog[50:])
res2 = mod2.smooth(params)
res3 = res2.apply(endog[:50], exog=exog[:50])
assert_equal(res1.specification, res3.specification)
for attr in [
'nobs', 'llf', 'llf_obs', 'loglikelihood_burn',
'cov_params_default'
]:
assert_equal(getattr(res3, attr), getattr(res1, attr))
for attr in [
'filtered_state', 'filtered_state_cov', 'predicted_state',
'predicted_state_cov', 'forecasts', 'forecasts_error',
'forecasts_error_cov', 'standardized_forecasts_error',
'forecasts_error_diffuse_cov', 'predicted_diffuse_state_cov',
'scaled_smoothed_estimator', 'scaled_smoothed_estimator_cov',
'smoothing_error', 'smoothed_state', 'smoothed_state_cov',
'smoothed_state_autocov', 'smoothed_measurement_disturbance',
'smoothed_state_disturbance',
'smoothed_measurement_disturbance_cov',
'smoothed_state_disturbance_cov'
]:
assert_equal(getattr(res3, attr), getattr(res1, attr))
assert_allclose(res3.forecast(10, exog=np.ones(10)),
res1.forecast(10, exog=np.ones(10)))
