def test_valid(self):
for args in self.valid:
assert_array_equal(tools.concat(*args[:-1]), args[-1])
def __init__(self, endog, exog, constraints=None, **kwargs):
# Standardize data
endog_using_pandas = _is_using_pandas(endog, None)
if not endog_using_pandas:
endog = np.asanyarray(endog)
exog_is_using_pandas = _is_using_pandas(exog, None)
if not exog_is_using_pandas:
exog = np.asarray(exog)
# Make sure we have 2-dimensional array
if exog.ndim == 1:
if not exog_is_using_pandas:
exog = exog[:, None]
else:
exog = pd.DataFrame(exog)
self.k_exog = exog.shape[1]
# Handle constraints
self.k_constraints = 0
self._r_matrix = self._q_matrix = None
if constraints is not None:
from patsy import DesignInfo
from statsmodels.base.data import handle_data
data = handle_data(endog, exog, **kwargs)
names = data.param_names
LC = DesignInfo(names).linear_constraint(constraints)
self._r_matrix, self._q_matrix = LC.coefs, LC.constants
self.k_constraints = self._r_matrix.shape[0]
constraint_endog = np.zeros((len(endog), len(self._r_matrix)))
if endog_using_pandas:
constraint_endog = pd.DataFrame(constraint_endog,
index=endog.index)
endog = concat([endog, constraint_endog], axis=1)
endog.values[:, 1:] = self._q_matrix[:, 0]
else:
endog[:, 1:] = self._q_matrix[:, 0]
# Handle coefficient initialization
kwargs.setdefault('initialization', 'diffuse')
# Initialize the state space representation
super(RecursiveLS, self).__init__(
endog, k_states=self.k_exog, exog=exog, **kwargs)
# Use univariate filtering by default
self.ssm.filter_univariate = True
# Concentrate the scale out of the likelihood function
self.ssm.filter_concentrated = True
# Setup the state space representation
self['design'] = np.zeros((self.k_endog, self.k_states, self.nobs))
self['design', 0] = self.exog[:, :, None].T
if self._r_matrix is not None:
self['design', 1:, :] = self._r_matrix[:, :, None]
self['transition'] = np.eye(self.k_states)
# Notice that the filter output does not depend on the measurement
# variance, so we set it here to 1
self['obs_cov', 0, 0] = 1.
self['transition'] = np.eye(self.k_states)
# Linear constraints are technically imposed by adding "fake" endog
# variables that are used during filtering, but for all model- and
# results-based purposes we want k_endog = 1.
if self._r_matrix is not None:
self.k_endog = 1
def test_valid(self):
for args in self.valid:
assert_array_equal(tools.concat(*args[:-1]), args[-1])
def test_invalid(self):
for args in self.invalid:
with pytest.raises(args[-1]):
tools.concat(*args[:-1])
def __init__(self, endog, exog, constraints=None, **kwargs):
# Standardize data
endog_using_pandas = _is_using_pandas(endog, None)
if not endog_using_pandas:
endog = np.asanyarray(endog)
exog_is_using_pandas = _is_using_pandas(exog, None)
if not exog_is_using_pandas:
exog = np.asarray(exog)
# Make sure we have 2-dimensional array
if exog.ndim == 1:
if not exog_is_using_pandas:
exog = exog[:, None]
else:
exog = pd.DataFrame(exog)
self.k_exog = exog.shape[1]
# Handle constraints
self.k_constraints = 0
self._r_matrix = self._q_matrix = None
if constraints is not None:
from patsy import DesignInfo
from statsmodels.base.data import handle_data
data = handle_data(endog, exog, **kwargs)
names = data.param_names
LC = DesignInfo(names).linear_constraint(constraints)
self._r_matrix, self._q_matrix = LC.coefs, LC.constants
self.k_constraints = self._r_matrix.shape[0]
constraint_endog = np.zeros((len(endog), len(self._r_matrix)))
if endog_using_pandas:
constraint_endog = pd.DataFrame(constraint_endog,
index=endog.index)
endog = concat([endog, constraint_endog], axis=1)
endog.values[:, 1:] = self._q_matrix[:, 0]
else:
endog[:, 1:] = self._q_matrix[:, 0]
# Handle coefficient initialization
kwargs.setdefault('initialization', 'diffuse')
# Initialize the state space representation
super(RecursiveLS, self).__init__(
endog, k_states=self.k_exog, exog=exog, **kwargs)
# Use univariate filtering by default
self.ssm.filter_univariate = True
# Concentrate the scale out of the likelihood function
self.ssm.filter_concentrated = True
# Setup the state space representation
self['design'] = np.zeros((self.k_endog, self.k_states, self.nobs))
self['design', 0] = self.exog[:, :, None].T
if self._r_matrix is not None:
self['design', 1:, :] = self._r_matrix[:, :, None]
self['transition'] = np.eye(self.k_states)
# Notice that the filter output does not depend on the measurement
# variance, so we set it here to 1
self['obs_cov', 0, 0] = 1.
self['transition'] = np.eye(self.k_states)
# Linear constraints are technically imposed by adding "fake" endog
# variables that are used during filtering, but for all model- and
# results-based purposes we want k_endog = 1.
if self._r_matrix is not None:
self.k_endog = 1
def test_invalid(self):
for args in self.invalid:
with pytest.raises(args[-1]):
tools.concat(*args[:-1])
