def test_no_endog():
# Test for RuntimeError when no endog is provided by the time filtering
# is initialized.
mod = Model(endog='test', k_states=1)
# directly call the _initialize_filter function
assert_raises(RuntimeError, mod._initialize_filter)
# indirectly call it through filtering
mod.initialize_approximate_diffuse()
assert_raises(RuntimeError, mod.filter)
def test_no_endog():
# Test for RuntimeError when no endog is provided by the time filtering
# is initialized.
mod = Model(endog='test', k_states=1)
# directly call the _initialize_filter function
assert_raises(RuntimeError, mod._initialize_filter)
# indirectly call it through filtering
mod.initialize_approximate_diffuse()
assert_raises(RuntimeError, mod.filter)
def test_loglike():
# Tests of invalid calls to the loglike function
endog = np.ones((10, 1))
mod = Model(endog, k_states=1, initialization='approximate_diffuse')
mod['design', :] = 1
mod['selection', :] = 1
mod['state_cov', :] = 1
# Test that self.memory_no_likelihood = True raises an error
mod.memory_no_likelihood = True
assert_raises(RuntimeError, mod.loglike)
assert_raises(RuntimeError, mod.loglikeobs)
def test_loglike():
# Tests of invalid calls to the loglike function
endog = np.ones((10,1))
mod = Model(endog, k_states=1, initialization='approximate_diffuse')
mod['design', :] = 1
mod['selection', :] = 1
mod['state_cov', :] = 1
# Test that self.memory_no_likelihood = True raises an error
mod.memory_no_likelihood = True
assert_raises(RuntimeError, mod.loglike)
assert_raises(RuntimeError, mod.loglikeobs)
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 __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 test_filter():
# Tests of invalid calls to the filter function
endog = np.ones((10,1))
mod = Model(endog, k_states=1, initialization='approximate_diffuse')
mod['design', :] = 1
mod['selection', :] = 1
mod['state_cov', :] = 1
# Test default filter results
res = mod.filter()
assert_equal(isinstance(res, SmootherResults), True)
# Test specified invalid results class
assert_raises(ValueError, mod.filter, results=object)
# Test specified valid results class
res = mod.filter(results=SmootherResults)
assert_equal(isinstance(res, SmootherResults), True)
def test_filter():
# Tests of invalid calls to the filter function
endog = np.ones((10, 1))
mod = Model(endog, k_states=1, initialization='approximate_diffuse')
mod['design', :] = 1
mod['selection', :] = 1
mod['state_cov', :] = 1
# Test default filter results
res = mod.filter()
assert_equal(isinstance(res, SmootherResults), True)
# Test specified invalid results class
assert_raises(ValueError, mod.filter, results=object)
# Test specified valid results class
res = mod.filter(results=SmootherResults)
assert_equal(isinstance(res, SmootherResults), True)
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 __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 test_slice_notation():
# Test setting and getting state space representation matrices using the
# slice notation.
endog = np.arange(10) * 1.0
mod = Model(endog, k_states=2)
# Test invalid __setitem__
def set_designs():
mod['designs'] = 1
def set_designs2():
mod['designs', 0, 0] = 1
def set_designs3():
mod[0] = 1
assert_raises(IndexError, set_designs)
assert_raises(IndexError, set_designs2)
assert_raises(IndexError, set_designs3)
# Test invalid __getitem__
assert_raises(IndexError, lambda: mod['designs'])
assert_raises(IndexError, lambda: mod['designs', 0, 0, 0])
assert_raises(IndexError, lambda: mod[0])
# Test valid __setitem__, __getitem__
assert_equal(mod.design[0, 0, 0], 0)
mod['design', 0, 0, 0] = 1
assert_equal(mod['design'].sum(), 1)
assert_equal(mod.design[0, 0, 0], 1)
assert_equal(mod['design', 0, 0, 0], 1)
# Test valid __setitem__, __getitem__ with unspecified time index
mod['design'] = np.zeros(mod['design'].shape)
assert_equal(mod.design[0, 0], 0)
mod['design', 0, 0] = 1
assert_equal(mod.design[0, 0], 1)
assert_equal(mod['design', 0, 0], 1)
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)
def test_cython():
# Test the cython _kalman_filter creation, re-creation, calling, etc.
# Check that datatypes are correct:
for prefix, dtype in tools.prefix_dtype_map.items():
endog = np.array(1., ndmin=2, dtype=dtype)
mod = Model(endog='test', k_states=1, dtype=dtype)
# Bind data and initialize the ?KalmanFilter object
mod.bind(endog)
mod._initialize_filter()
# Check that the dtype and prefix are correct
assert_equal(mod.prefix, prefix)
assert_equal(mod.dtype, dtype)
# Test that a dKalmanFilter instance was created
assert_equal(prefix in mod._kalman_filters, True)
kf = mod._kalman_filters[prefix]
assert_equal(isinstance(kf, tools.prefix_kalman_filter_map[prefix]), True)
# Test that the default returned _kalman_filter is the above instance
assert_equal(mod._kalman_filter, kf)
# Check that upcasting datatypes / ?KalmanFilter works (e.g. d -> z)
mod = Model(endog='test', k_states=1)
# Default dtype is float
assert_equal(mod.prefix, 'd')
assert_equal(mod.dtype, np.float64)
# Prior to initialization, no ?KalmanFilter exists
assert_equal(mod._kalman_filter, None)
# Bind data and initialize the ?KalmanFilter object
endog = np.ascontiguousarray(np.array([1., 2.], dtype=np.float64))
mod.bind(endog)
mod._initialize_filter()
kf = mod._kalman_filters['d']
# Rebind data, still float, check that we haven't changed
mod.bind(endog)
mod._initialize_filter()
assert_equal(mod._kalman_filter, kf)
# Force creating new ?Statespace and ?KalmanFilter, by changing the
# time-varying character of an array
mod.design = np.zeros((1,1,2))
mod._initialize_filter()
assert_equal(mod._kalman_filter == kf, False)
kf = mod._kalman_filters['d']
# Rebind data, now complex, check that the ?KalmanFilter instance has
# changed
endog = np.ascontiguousarray(np.array([1., 2.], dtype=np.complex128))
mod.bind(endog)
assert_equal(mod._kalman_filter == kf, False)
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
)
def test_cython():
# Test the cython _kalman_filter creation, re-creation, calling, etc.
# Check that datatypes are correct:
for prefix, dtype in tools.prefix_dtype_map.items():
endog = np.array(1., ndmin=2, dtype=dtype)
mod = Model(endog='test', k_states=1, dtype=dtype)
# Bind data and initialize the ?KalmanFilter object
mod.bind(endog)
mod._initialize_filter()
# Check that the dtype and prefix are correct
assert_equal(mod.prefix, prefix)
assert_equal(mod.dtype, dtype)
# Test that a dKalmanFilter instance was created
assert_equal(prefix in mod._kalman_filters, True)
kf = mod._kalman_filters[prefix]
assert_equal(isinstance(kf, tools.prefix_kalman_filter_map[prefix]),
True)
# Test that the default returned _kalman_filter is the above instance
assert_equal(mod._kalman_filter, kf)
# Check that upcasting datatypes / ?KalmanFilter works (e.g. d -> z)
mod = Model(endog='test', k_states=1)
# Default dtype is float
assert_equal(mod.prefix, 'd')
assert_equal(mod.dtype, np.float64)
# Prior to initialization, no ?KalmanFilter exists
assert_equal(mod._kalman_filter, None)
# Bind data and initialize the ?KalmanFilter object
endog = np.ascontiguousarray(np.array([1., 2.], dtype=np.float64))
mod.bind(endog)
mod._initialize_filter()
kf = mod._kalman_filters['d']
# Rebind data, still float, check that we haven't changed
mod.bind(endog)
mod._initialize_filter()
assert_equal(mod._kalman_filter, kf)
# Force creating new ?Statespace and ?KalmanFilter, by changing the
# time-varying character of an array
mod.design = np.zeros((1, 1, 2))
mod._initialize_filter()
assert_equal(mod._kalman_filter == kf, False)
kf = mod._kalman_filters['d']
# Rebind data, now complex, check that the ?KalmanFilter instance has
# changed
endog = np.ascontiguousarray(np.array([1., 2.], dtype=np.complex128))
mod.bind(endog)
assert_equal(mod._kalman_filter == kf, False)
def test_predict():
# Tests of invalid calls to the predict function
warnings.simplefilter("always")
endog = np.ones((10,1))
mod = Model(endog, k_states=1, initialization='approximate_diffuse')
mod['design', :] = 1
mod['obs_intercept'] = np.zeros((1,10))
mod['selection', :] = 1
mod['state_cov', :] = 1
# Check that we need both forecasts and predicted output for prediction
mod.memory_no_forecast = True
res = mod.filter()
assert_raises(ValueError, res.predict)
mod.memory_no_forecast = False
mod.memory_no_predicted = True
res = mod.filter()
assert_raises(ValueError, res.predict)
mod.memory_no_predicted = False
# Now get a clean filter object
res = mod.filter()
# Check that start < 0 is an error
assert_raises(ValueError, res.predict, start=-1)
# Check that end < start is an error
assert_raises(ValueError, res.predict, start=2, end=1)
# Check that dynamic < 0 is an error
assert_raises(ValueError, res.predict, dynamic=-1)
# Check that dynamic > end is an warning
with warnings.catch_warnings(record=True) as w:
res.predict(end=1, dynamic=2)
message = ('Dynamic prediction specified to begin after the end of'
' prediction, and so has no effect.')
assert_equal(str(w[0].message), message)
# Check that dynamic > nobs is an warning
with warnings.catch_warnings(record=True) as w:
res.predict(end=11, dynamic=11, obs_intercept=np.zeros((1,1)))
message = ('Dynamic prediction specified to begin during'
' out-of-sample forecasting period, and so has no'
' effect.')
assert_equal(str(w[0].message), message)
# Check for a warning when providing a non-used statespace matrix
with warnings.catch_warnings(record=True) as w:
res.predict(end=res.nobs+1, design=True, obs_intercept=np.zeros((1,1)))
message = ('Model has time-invariant design matrix, so the design'
' argument to `predict` has been ignored.')
assert_equal(str(w[0].message), message)
# Check that an error is raised when a new time-varying matrix is not
# provided
assert_raises(ValueError, res.predict, end=res.nobs+1)
# Check that an error is raised when a non-two-dimensional obs_intercept
# is given
assert_raises(ValueError, res.predict, end=res.nobs+1,
obs_intercept=np.zeros(1))
# Check that an error is raised when an obs_intercept with incorrect length
# is given
assert_raises(ValueError, res.predict, end=res.nobs+1,
obs_intercept=np.zeros(2))
# Check that start=None gives start=0 and end=None gives end=nobs
assert_equal(res.predict().forecasts.shape, (1,res.nobs))
# Check that dynamic=True begins dynamic prediction immediately
# TODO just a smoke test
res.predict(dynamic=True)
# Check that full_results=True yields a SmootherResults object
assert_equal(isinstance(res.predict(), PredictionResults), True)
# Check that an error is raised when a non-two-dimensional obs_cov
# is given
# ...and...
# Check that an error is raised when an obs_cov with incorrect length
# is given
mod = Model(endog, k_states=1, initialization='approximate_diffuse')
mod['design', :] = 1
mod['obs_cov'] = np.zeros((1,1,10))
mod['selection', :] = 1
mod['state_cov', :] = 1
res = mod.filter()
assert_raises(ValueError, res.predict, end=res.nobs+1,
obs_cov=np.zeros((1,1)))
assert_raises(ValueError, res.predict, end=res.nobs+1,
obs_cov=np.zeros((1,1,2)))
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)
def test_predict():
# Tests of invalid calls to the predict function
warnings.simplefilter("always")
endog = np.ones((10, 1))
mod = Model(endog, k_states=1, initialization='approximate_diffuse')
mod['design', :] = 1
mod['obs_intercept'] = np.zeros((1, 10))
mod['selection', :] = 1
mod['state_cov', :] = 1
# Check that we need both forecasts and predicted output for prediction
mod.memory_no_forecast = True
res = mod.filter()
assert_raises(ValueError, res.predict)
mod.memory_no_forecast = False
mod.memory_no_predicted = True
res = mod.filter()
assert_raises(ValueError, res.predict)
mod.memory_no_predicted = False
# Now get a clean filter object
res = mod.filter()
# Check that start < 0 is an error
assert_raises(ValueError, res.predict, start=-1)
# Check that end < start is an error
assert_raises(ValueError, res.predict, start=2, end=1)
# Check that dynamic < 0 is an error
assert_raises(ValueError, res.predict, dynamic=-1)
# Check that dynamic > end is an warning
with warnings.catch_warnings(record=True) as w:
res.predict(end=1, dynamic=2)
message = ('Dynamic prediction specified to begin after the end of'
' prediction, and so has no effect.')
assert_equal(str(w[0].message), message)
# Check that dynamic > nobs is an warning
with warnings.catch_warnings(record=True) as w:
res.predict(end=11, dynamic=11, obs_intercept=np.zeros((1, 1)))
message = ('Dynamic prediction specified to begin during'
' out-of-sample forecasting period, and so has no'
' effect.')
assert_equal(str(w[0].message), message)
# Check for a warning when providing a non-used statespace matrix
with warnings.catch_warnings(record=True) as w:
res.predict(end=res.nobs + 1,
design=True,
obs_intercept=np.zeros((1, 1)))
message = ('Model has time-invariant design matrix, so the design'
' argument to `predict` has been ignored.')
assert_equal(str(w[0].message), message)
# Check that an error is raised when a new time-varying matrix is not
# provided
assert_raises(ValueError, res.predict, end=res.nobs + 1)
# Check that an error is raised when a non-two-dimensional obs_intercept
# is given
assert_raises(ValueError,
res.predict,
end=res.nobs + 1,
obs_intercept=np.zeros(1))
# Check that an error is raised when an obs_intercept with incorrect length
# is given
assert_raises(ValueError,
res.predict,
end=res.nobs + 1,
obs_intercept=np.zeros(2))
# Check that start=None gives start=0 and end=None gives end=nobs
assert_equal(res.predict().forecasts.shape, (1, res.nobs))
# Check that dynamic=True begins dynamic prediction immediately
# TODO just a smoke test
res.predict(dynamic=True)
# Check that full_results=True yields a SmootherResults object
assert_equal(isinstance(res.predict(), PredictionResults), True)
# Check that an error is raised when a non-two-dimensional obs_cov
# is given
# ...and...
# Check that an error is raised when an obs_cov with incorrect length
# is given
mod = Model(endog, k_states=1, initialization='approximate_diffuse')
mod['design', :] = 1
mod['obs_cov'] = np.zeros((1, 1, 10))
mod['selection', :] = 1
mod['state_cov', :] = 1
res = mod.filter()
assert_raises(ValueError,
res.predict,
end=res.nobs + 1,
obs_cov=np.zeros((1, 1)))
assert_raises(ValueError,
res.predict,
end=res.nobs + 1,
obs_cov=np.zeros((1, 1, 2)))
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
)
