def test_mle_reg():
endog = np.arange(100)*1.0
exog = endog*2
# Make the fit not-quite-perfect
endog[::2] += 0.01
endog[1::2] -= 0.01
with warnings.catch_warnings(record=True) as w:
mod1 = UnobservedComponents(endog, irregular=True, exog=exog, mle_regression=False)
res1 = mod1.fit(disp=-1)
mod2 = UnobservedComponents(endog, irregular=True, exog=exog, mle_regression=True)
res2 = mod2.fit(disp=-1)
assert_allclose(res1.regression_coefficients.filtered[0, -1], 0.5, atol=1e-5)
assert_allclose(res2.params[1], 0.5, atol=1e-5)
def test_specifications():
endog = [1, 2]
# Test that when nothing specified, a warning is issued and the model that
# is fit is one with irregular=True and nothing else.
warnings.simplefilter("always")
with warnings.catch_warnings(record=True) as w:
mod = UnobservedComponents(endog)
message = ("Specified model does not contain a stochastic element;"
" irregular component added.")
assert_equal(str(w[0].message), message)
assert_equal(mod.trend_specification, 'irregular')
# Test an invalid string trend specification
assert_raises(ValueError, UnobservedComponents, endog, 'invalid spec')
# Test that if a trend component is specified without a level component,
# a warning is issued and a deterministic level component is added
with warnings.catch_warnings(record=True) as w:
mod = UnobservedComponents(endog, trend=True, irregular=True)
message = ("Trend component specified without level component;"
" deterministic level component added.")
assert_equal(str(w[0].message), message)
assert_equal(mod.trend_specification, 'deterministic trend')
# Test that if a string specification is provided, a warning is issued if
# the boolean attributes are also specified
trend_attributes = [
'irregular', 'trend', 'stochastic_level', 'stochastic_trend'
]
for attribute in trend_attributes:
with warnings.catch_warnings(record=True) as w:
kwargs = {attribute: True}
mod = UnobservedComponents(endog, 'deterministic trend', **kwargs)
message = ("Value of `%s` may be overridden when the trend"
" component is specified using a model string." %
attribute)
assert_equal(str(w[0].message), message)
# Test that a seasonal with period less than two is invalid
assert_raises(ValueError, UnobservedComponents, endog, seasonal=1)
def test_start_params():
# Test that the behavior is correct for multiple exogenous and / or
# autoregressive components
# Parameters
nobs = int(1e4)
beta = np.r_[10, -2]
phi = np.r_[0.5, 0.1]
# Generate data
np.random.seed(1234)
exog = np.c_[np.ones(nobs), np.arange(nobs) * 1.0]
eps = np.random.normal(size=nobs)
endog = np.zeros(nobs + 2)
for t in range(1, nobs):
endog[t + 1] = phi[0] * endog[t] + phi[1] * endog[t - 1] + eps[t]
endog = endog[2:]
endog += np.dot(exog, beta)
# Now just test that the starting parameters are approximately what they
# ought to be (could make this arbitrarily precise by increasing nobs,
# but that would slow down the test for no real gain)
mod = UnobservedComponents(endog, exog=exog, autoregressive=2)
assert_allclose(mod.start_params, [1., 0.5, 0.1, 10, -2], atol=1e-1)
def test_mle_reg():
endog = np.arange(100) * 1.0
exog = endog * 2
# Make the fit not-quite-perfect
endog[::2] += 0.01
endog[1::2] -= 0.01
with warnings.catch_warnings(record=True) as w:
mod1 = UnobservedComponents(endog,
irregular=True,
exog=exog,
mle_regression=False)
res1 = mod1.fit(disp=-1)
mod2 = UnobservedComponents(endog,
irregular=True,
exog=exog,
mle_regression=True)
res2 = mod2.fit(disp=-1)
assert_allclose(res1.regression_coefficients.filtered[0, -1],
0.5,
atol=1e-5)
assert_allclose(res2.params[1], 0.5, atol=1e-5)
def run_ucm(name):
true = getattr(results_structural, name)
for model in true['models']:
kwargs = model.copy()
kwargs.update(true['kwargs'])
# Make a copy of the data
values = dta.copy()
freq = kwargs.pop('freq', None)
if freq is not None:
values.index = pd.date_range(start='1959-01-01',
periods=len(dta),
freq=freq)
# Test pandas exog
if 'exog' in kwargs:
# Default value here is pd.Series object
exog = np.log(values['realgdp'])
# Also allow a check with a 1-dim numpy array
if kwargs['exog'] == 'numpy':
exog = exog.values.squeeze()
kwargs['exog'] = exog
# Create the model
mod = UnobservedComponents(values['unemp'], **kwargs)
# Smoke test for starting parameters, untransform, transform
# Also test that transform and untransform are inverses
mod.start_params
assert_allclose(
mod.start_params,
mod.transform_params(mod.untransform_params(mod.start_params)))
# Fit the model at the true parameters
res_true = mod.filter(true['params'])
# Check that the cycle bounds were computed correctly
freqstr = freq[0] if freq is not None else values.index.freqstr[0]
if freqstr == 'A':
cycle_period_bounds = (1.5, 12)
elif freqstr == 'Q':
cycle_period_bounds = (1.5 * 4, 12 * 4)
elif freqstr == 'M':
cycle_period_bounds = (1.5 * 12, 12 * 12)
else:
# If we have no information on data frequency, require the
# cycle frequency to be between 0 and pi
cycle_period_bounds = (2, np.inf)
# Test that the cycle frequency bound is correct
assert_equal(mod.cycle_frequency_bound,
(2 * np.pi / cycle_period_bounds[1],
2 * np.pi / cycle_period_bounds[0]))
# Test that the likelihood is correct
rtol = true.get('rtol', 1e-7)
atol = true.get('atol', 0)
assert_allclose(res_true.llf, true['llf'], rtol=rtol, atol=atol)
# Smoke test for plot_components
if have_matplotlib:
fig = res_true.plot_components()
plt.close(fig)
# Now fit the model via MLE
with warnings.catch_warnings(record=True) as w:
res = mod.fit(disp=-1)
# If we found a higher likelihood, no problem; otherwise check
# that we're very close to that found by R
if res.llf <= true['llf']:
assert_allclose(res.llf, true['llf'], rtol=1e-4)
# Smoke test for summary
res.summary()
def run_ucm(name):
true = getattr(results_structural, name)
for model in true['models']:
kwargs = model.copy()
kwargs.update(true['kwargs'])
# Make a copy of the data
values = dta.copy()
freq = kwargs.pop('freq', None)
if freq is not None:
values.index = pd.date_range(start='1959-01-01', periods=len(dta),
freq=freq)
# Test pandas exog
if 'exog' in kwargs:
# Default value here is pd.Series object
exog = np.log(values['realgdp'])
# Also allow a check with a 1-dim numpy array
if kwargs['exog'] == 'numpy':
exog = exog.values.squeeze()
kwargs['exog'] = exog
# Create the model
mod = UnobservedComponents(values['unemp'], **kwargs)
# Smoke test for starting parameters, untransform, transform
# Also test that transform and untransform are inverses
mod.start_params
assert_allclose(mod.start_params, mod.transform_params(mod.untransform_params(mod.start_params)))
# Fit the model at the true parameters
res_true = mod.filter(true['params'])
# Check that the cycle bounds were computed correctly
freqstr = freq[0] if freq is not None else values.index.freqstr[0]
if freqstr == 'A':
cycle_period_bounds = (1.5, 12)
elif freqstr == 'Q':
cycle_period_bounds = (1.5*4, 12*4)
elif freqstr == 'M':
cycle_period_bounds = (1.5*12, 12*12)
else:
# If we have no information on data frequency, require the
# cycle frequency to be between 0 and pi
cycle_period_bounds = (2, np.inf)
# Test that the cycle frequency bound is correct
assert_equal(mod.cycle_frequency_bound,
(2*np.pi / cycle_period_bounds[1],
2*np.pi / cycle_period_bounds[0])
)
# Test that the likelihood is correct
rtol = true.get('rtol', 1e-7)
atol = true.get('atol', 0)
assert_allclose(res_true.llf, true['llf'], rtol=rtol, atol=atol)
# Smoke test for plot_components
if have_matplotlib:
fig = res_true.plot_components()
plt.close(fig)
# Now fit the model via MLE
with warnings.catch_warnings(record=True) as w:
res = mod.fit(disp=-1)
# If we found a higher likelihood, no problem; otherwise check
# that we're very close to that found by R
if res.llf <= true['llf']:
assert_allclose(res.llf, true['llf'], rtol=1e-4)
# Smoke test for summary
res.summary()