def test_adf_critical_value(self):
adf = ADF(self.inflation, trend='c', lags=3)
adf_cv = adf.critical_values
temp = polyval(tau_2010['c'][0, :, ::-1].T, 1. / adf.nobs)
cv = {'1%': temp[0], '5%': temp[1], '10%': temp[2]}
for k, v in iteritems(cv):
assert_almost_equal(v, adf_cv[k])
def _repr_html_(self):
"""HTML representation for IPython Notebook"""
descr = self._model_description()
html = '<strong>' + self.name + '</strong>('
for key, val in iteritems(descr):
html += '<strong>' + key + ': </strong>' + val + ',\n'
html += '<strong>ID: </strong> ' + hex(id(self)) + ')'
return html
def _info_to_str(model, info, is_repr=False, is_html=False):
if is_html:
model = '<strong>' + model + '</strong>'
_str = model + '('
for k, v in iteritems(info):
if k.lower() != 'id' or is_repr:
if is_html:
k = '<strong>' + k + '</strong>'
_str += k + ': ' + v + ', '
return _str[:-2] + ')'
def _info_to_str(model, info, is_repr=False, is_html=False):
if is_html:
model = "<strong>" + model + "</strong>"
_str = model + "("
for k, v in iteritems(info):
if k.lower() != "id" or is_repr:
if is_html:
k = "<strong>" + k + "</strong>"
_str += k + ": " + v + ", "
return _str[:-2] + ")"
def __str__(self):
descr = self._model_description()
descr_str = self.name + '('
for key, val in iteritems(descr):
if val:
if key:
descr_str += key + ': ' + val + ', '
descr_str = descr_str[:-2] # Strip final ', '
descr_str += ')'
return descr_str
def test_multiple_lags(self):
"""Smoke test to ensure models estimate with multiple lags"""
vp = {'garch': GARCH, 'egarch': EGARCH, 'harch': HARCH, 'arch': ARCH}
cm = ConstantMean(self.y)
for name, process in iteritems(vp):
cm.volatility = process()
cm.fit(update_freq=0, disp='off')
for p in [1, 2, 3]:
for o in [1, 2, 3]:
for q in [1, 2, 3]:
if name in ('arch', ):
cm.volatility = process(p=p + o + q)
cm.fit(update_freq=0, disp='off')
elif name in ('harch', ):
cm.volatility = process(lags=[p, p + o, p + o + q])
cm.fit(update_freq=0, disp='off')
else:
cm.volatility = process(p=p, o=o, q=q)
cm.fit(update_freq=0, disp='off')
def __init__(self, *args, **kwargs):
self._random_state = None
self._args = args
self._kwargs = kwargs
random_state = self._kwargs.pop('random_state', None)
self.random_state = random_state if random_state is not None else RandomState(
)
self._initial_state = self._random_state.get_state()
if args:
self._num_items = len(args[0])
elif kwargs:
key = list(kwargs.keys())[0]
self._num_items = len(kwargs[key])
all_args = list(args)
all_args.extend([v for v in itervalues(kwargs)])
if self._common_size_required:
for arg in all_args:
if len(arg) != self._num_items:
raise ValueError("All inputs must have the same number of "
"elements in axis 0")
self._index = np.arange(self._num_items)
self._parameters = []
self._seed = None
self.pos_data = args
self.kw_data = kwargs
self.data = (args, kwargs)
self._base = None
self._results = None
self._studentized_results = None
self._last_func = None
self._name = 'IID Bootstrap'
for key, value in iteritems(kwargs):
attr = getattr(self, key, None)
if attr is None:
self.__setattr__(key, value)
else:
raise ValueError(key + ' is a reserved name')
def __init__(self, *args, **kwargs):
self._random_state = None
self._args = args
self._kwargs = kwargs
random_state = self._kwargs.pop('random_state', None)
self.random_state = random_state if random_state is not None else RandomState()
self._initial_state = self._random_state.get_state()
if args:
self._num_items = len(args[0])
elif kwargs:
key = list(kwargs.keys())[0]
self._num_items = len(kwargs[key])
all_args = list(args)
all_args.extend([v for v in itervalues(kwargs)])
if self._common_size_required:
for arg in all_args:
if len(arg) != self._num_items:
raise ValueError("All inputs must have the same number of "
"elements in axis 0")
self._index = np.arange(self._num_items)
self._parameters = []
self._seed = None
self.pos_data = args
self.kw_data = kwargs
self.data = (args, kwargs)
self._base = None
self._results = None
self._studentized_results = None
self._last_func = None
self._name = 'IID Bootstrap'
for key, value in iteritems(kwargs):
attr = getattr(self, key, None)
if attr is None:
self.__setattr__(key, value)
else:
raise ValueError(key + ' is a reserved name')
def test_multiple_lags(self):
"""Smoke test to ensure models estimate with multiple lags"""
vp = {'garch': GARCH,
'egarch': EGARCH,
'harch': HARCH,
'arch': ARCH}
cm = ConstantMean(self.y)
for name, process in iteritems(vp):
cm.volatility = process()
cm.fit(update_freq=0, disp='off')
for p in [1, 2, 3]:
for o in [1, 2, 3]:
for q in [1, 2, 3]:
if name in ('arch',):
cm.volatility = process(p=p + o + q)
cm.fit(update_freq=0, disp='off')
elif name in ('harch',):
cm.volatility = process(lags=[p, p + o, p + o + q])
cm.fit(update_freq=0, disp='off')
else:
cm.volatility = process(p=p, o=o, q=q)
cm.fit(update_freq=0, disp='off')
def _loo_jackknife(func, nobs, args, kwargs):
"""
Leave one out jackknife estimation
Parameters
----------
func : callable
Function that computes parameters. Called using func(*args, **kwargs)
nobs : int
Number of observation in the data
args : list
List of positional inputs (arrays, Series or DataFrames)
kwargs : dict
List of keyword inputs (arrays, Series or DataFrames)
Returns
-------
results : ndarray
Array containing the jackknife results where row i corresponds to
leaving observation i out of the sample
"""
results = []
for i in range(nobs):
items = np.r_[0:i, i + 1:nobs]
args_copy = []
for arg in args:
if isinstance(arg, (pd.Series, pd.DataFrame)):
args_copy.append(arg.iloc[items])
else:
args_copy.append(arg[items])
kwargs_copy = {}
for k, v in iteritems(kwargs):
if isinstance(v, (pd.Series, pd.DataFrame)):
kwargs_copy[k] = v.iloc[items]
else:
kwargs_copy[k] = v[items]
results.append(func(*args_copy, **kwargs_copy))
return np.array(results)
def _loo_jackknife(func, nobs, args, kwargs):
"""
Leave one out jackknife estimation
Parameters
----------
func : callable
Function that computes parameters. Called using func(*args, **kwargs)
nobs : int
Number of observation in the data
args : list
List of positional inputs (arrays, Series or DataFrames)
kwargs : dict
List of keyword inputs (arrays, Series or DataFrames)
Returns
-------
results : ndarray
Array containing the jackknife results where row i corresponds to
leaving observation i out of the sample
"""
results = []
for i in range(nobs):
items = np.r_[0:i, i + 1:nobs]
args_copy = []
for arg in args:
if isinstance(arg, (pd.Series, pd.DataFrame)):
args_copy.append(arg.iloc[items])
else:
args_copy.append(arg[items])
kwargs_copy = {}
for k, v in iteritems(kwargs):
if isinstance(v, (pd.Series, pd.DataFrame)):
kwargs_copy[k] = v.iloc[items]
else:
kwargs_copy[k] = v[items]
results.append(func(*args_copy, **kwargs_copy))
return np.array(results)
def _resample(self):
"""
Resample all data using the values in _index
"""
indices = self._index
pos_data = []
for values in self._args:
if isinstance(values, (pd.Series, pd.DataFrame)):
pos_data.append(values.iloc[indices])
else:
pos_data.append(values[indices])
named_data = {}
for key, values in iteritems(self._kwargs):
if isinstance(values, (pd.Series, pd.DataFrame)):
named_data[key] = values.iloc[indices]
else:
named_data[key] = values[indices]
setattr(self, key, named_data[key])
self.pos_data = pos_data
self.kw_data = named_data
self.data = (pos_data, named_data)
return self.data
def _resample(self):
"""
Resample all data using the values in _index
"""
pos_indices, kw_indices = self._index
pos_data = []
for i, values in enumerate(self._args):
if isinstance(values, (pd.Series, pd.DataFrame)):
pos_data.append(values.iloc[pos_indices[i]])
else:
pos_data.append(values[pos_indices[i]])
named_data = {}
for key, values in iteritems(self._kwargs):
if isinstance(values, (pd.Series, pd.DataFrame)):
named_data[key] = values.iloc[kw_indices[key]]
else:
named_data[key] = values[kw_indices[key]]
setattr(self, key, named_data[key])
self.pos_data = pos_data
self.kw_data = named_data
self.data = (pos_data, named_data)
return self.data
'ct': [4.6476, -2.8932, 0.5832, -0.09990],
'ctt': [4.4599, -1.8635, 0.2126, -0.06070]
}
# These are Table 6 from MacKinnon (1994).
# These are well-behaved in the right tail.
# the approximation function is
# p = norm.cdf(d_0 + d_1 * z + d_2*z**2 + d_3*z**3 + d_4*z**4)
adf_z_large_p = {
'nc': [0.4927, 6.906, 13.2331, 12.099, 0],
'c': [1.7157, 0.5536, 4.5518, 2.2466, 4.2537],
'ct': [2.7119, 0.4594, 2.3747, 0.7488, 0.9333],
'ctt': [3.4216, 0.4170, 1.6939, 0.4203, 0.4153]
}
adf_z_large_p_scale = asarray([1.0, 1.0, 1e-2, 1e-3, 1e-5])
for k, v in iteritems(adf_z_large_p):
adf_z_large_p[k] = asarray(adf_z_large_p[k]) * adf_z_large_p_scale
adf_z_cv_approx = {
'ctt':
asarray([[-36.5942942, 353.85126482, -2510.01976534, 9844.80839543],
[-28.11046156, 211.97559729, -1238.23377747, 4268.99024104],
[-24.18543008, 158.50672198, -837.17807309, 2729.45518704]]),
'c':
asarray([[-20.62450004, 118.83362675, -534.54927244, 1471.9177227],
[-14.09312678, 57.9842846, -196.6401263, 420.09313558],
[-11.25012522, 38.19512941, -111.45098086, 226.91220644]]),
'nc':
asarray([[-13.2907657, 323.225921, -8318.89213, 79857.3965],
[-7.81966697, 217.366594, -5554.92749, 52618.7129],
[-5.56726973, 166.689373, -4194.13783, 39301.1304]]),
"ct": [4.6476, -2.8932, 0.5832, -0.09990],
"ctt": [4.4599, -1.8635, 0.2126, -0.06070],
}
# These are Table 6 from MacKinnon (1994).
# These are well-behaved in the right tail.
# the approximation function is
# p = norm.cdf(d_0 + d_1 * z + d_2*z**2 + d_3*z**3 + d_4*z**4)
adf_z_large_p = {
"nc": [0.4927, 6.906, 13.2331, 12.099, 0],
"c": [1.7157, 0.5536, 4.5518, 2.2466, 4.2537],
"ct": [2.7119, 0.4594, 2.3747, 0.7488, 0.9333],
"ctt": [3.4216, 0.4170, 1.6939, 0.4203, 0.4153],
}
adf_z_large_p_scale = asarray([1.0, 1.0, 1e-2, 1e-3, 1e-5])
for k, v in iteritems(adf_z_large_p):
adf_z_large_p[k] = asarray(adf_z_large_p[k]) * adf_z_large_p_scale
adf_z_cv_approx = {
"ctt": asarray(
[
[-36.5942942, 353.85126482, -2510.01976534, 9844.80839543],
[-28.11046156, 211.97559729, -1238.23377747, 4268.99024104],
[-24.18543008, 158.50672198, -837.17807309, 2729.45518704],
]
),
"c": asarray(
[
[-20.62450004, 118.83362675, -534.54927244, 1471.9177227],
[-14.09312678, 57.9842846, -196.6401263, 420.09313558],
[-11.25012522, 38.19512941, -111.45098086, 226.91220644],
