def init_param_spatial(self, restriction='shomo', groups=None,
use_target=False, method='SLSQP', cfree=False,
use_penalty=False):
"""Estimate scalar BEKK with variance targeting.
Parameters
----------
restriction : str
Restriction on parameters.
Must be
- 'hetero' (heterogeneous)
- 'ghomo' (group homogeneous)
- 'h**o' (homogeneous)
- 'shomo' (scalar homogeneous)
cfree : bool
Whether to leave C matrix free (True) or not (False)
groups : list of lists of tuples
Encoded groups of items
method : str
Optimization method. See scipy.optimize.minimize
Returns
-------
ParamSpatial instance
Parameter object
"""
param = ParamSpatial.from_groups(groups=groups,
target=estimate_uvar(self.innov),
abstart=(.2, .7))
if restriction == 'shomo':
return param
kwargs = {'use_target': False, 'groups': groups,
'use_target': use_target,
'use_penalty': use_penalty, 'model': 'spatial',
'cfree': cfree, 'method': method}
est_partial = partial(self.estimate, **kwargs)
if restriction in ('h**o', 'ghomo', 'hetero'):
result = est_partial(param_start=param, restriction='shomo')
param = result.param_final
if restriction in ('ghomo', 'hetero'):
result = est_partial(param_start=param, restriction='h**o')
param = result.param_final
if restriction in ('hetero'):
result = est_partial(param_start=param, restriction='ghomo')
param = result.param_final
return param
def estimate_loop(self,
model='standard',
use_target=True,
groups=None,
restriction='scalar',
cfree=False,
method='SLSQP',
ngrid=2,
use_penalty=False):
"""Estimate parameters starting from a grid of a and b.
Parameters
----------
model : str
Specific model to estimate.
Must be
- 'standard'
- 'spatial'
restriction : str
Restriction on parameters.
Must be
- 'full' = 'diagonal'
- 'group'
- 'scalar'
groups : list of lists of tuples
Encoded groups of items
use_target : bool
Whether to use variance targeting (True) or not (False)
cfree : bool
Whether to leave C matrix free (True) or not (False)
method : str
Optimization method. See scipy.optimize.minimize
ngrid : int
Number of starting values in one dimension
use_penalty : bool
Whether to include penalty term in the likelihood
Returns
-------
BEKKResults instance
Estimation results object
"""
target = estimate_uvar(self.innov)
nstocks = self.innov.shape[1]
achoice = np.linspace(.01, .5, ngrid)
bchoice = np.linspace(.1, .9, ngrid)
out = dict()
for abstart in itertools.product(achoice, bchoice):
if model == 'spatial':
param = ParamSpatial.from_groups(groups=groups,
target=target,
abstart=abstart)
if model == 'standard':
param = ParamStandard(nstocks=nstocks,
target=target,
abstart=abstart)
if param.constraint() >= 1:
continue
result = self.estimate(param_start=param,
method=method,
use_target=use_target,
cfree=cfree,
model=model,
restriction=restriction,
groups=groups,
use_penalty=use_penalty)
out[abstart] = (result.opt_out.fun, result)
df = pd.DataFrame.from_dict(out, orient='index')
return df.sort_values(by=0).iloc[0, 1]
def init_param_spatial(self,
restriction='shomo',
groups=None,
use_target=False,
method='SLSQP',
cfree=False,
use_penalty=False):
"""Estimate scalar BEKK with variance targeting.
Parameters
----------
restriction : str
Restriction on parameters.
Must be
- 'hetero' (heterogeneous)
- 'ghomo' (group homogeneous)
- 'h**o' (homogeneous)
- 'shomo' (scalar homogeneous)
cfree : bool
Whether to leave C matrix free (True) or not (False)
groups : list of lists of tuples
Encoded groups of items
method : str
Optimization method. See scipy.optimize.minimize
Returns
-------
ParamSpatial instance
Parameter object
"""
param = ParamSpatial.from_groups(groups=groups,
target=estimate_uvar(self.innov),
abstart=(.2, .7))
if restriction == 'shomo':
return param
kwargs = {
'use_target': False,
'groups': groups,
'use_target': use_target,
'use_penalty': use_penalty,
'model': 'spatial',
'cfree': cfree,
'method': method
}
est_partial = partial(self.estimate, **kwargs)
if restriction in ('h**o', 'ghomo', 'hetero'):
result = est_partial(param_start=param, restriction='shomo')
param = result.param_final
if restriction in ('ghomo', 'hetero'):
result = est_partial(param_start=param, restriction='h**o')
param = result.param_final
if restriction in ('hetero'):
result = est_partial(param_start=param, restriction='ghomo')
param = result.param_final
return param
def estimate_loop(self, model='standard', use_target=True, groups=None,
restriction='scalar', cfree=False,
method='SLSQP', ngrid=2, use_penalty=False):
"""Estimate parameters starting from a grid of a and b.
Parameters
----------
model : str
Specific model to estimate.
Must be
- 'standard'
- 'spatial'
restriction : str
Restriction on parameters.
Must be
- 'full' = 'diagonal'
- 'group'
- 'scalar'
groups : list of lists of tuples
Encoded groups of items
use_target : bool
Whether to use variance targeting (True) or not (False)
cfree : bool
Whether to leave C matrix free (True) or not (False)
method : str
Optimization method. See scipy.optimize.minimize
ngrid : int
Number of starting values in one dimension
use_penalty : bool
Whether to include penalty term in the likelihood
Returns
-------
BEKKResults instance
Estimation results object
"""
target = estimate_uvar(self.innov)
nstocks = self.innov.shape[1]
achoice = np.linspace(.01, .5, ngrid)
bchoice = np.linspace(.1, .9, ngrid)
out = dict()
for abstart in itertools.product(achoice, bchoice):
if model == 'spatial':
param = ParamSpatial.from_groups(groups=groups,
target=target,
abstart=abstart)
if model == 'standard':
param = ParamStandard(nstocks=nstocks, target=target,
abstart=abstart)
if param.constraint() >= 1:
continue
result = self.estimate(param_start=param, method=method,
use_target=use_target, cfree=cfree,
model=model, restriction=restriction,
groups=groups, use_penalty=use_penalty)
out[abstart] = (result.opt_out.fun, result)
df = pd.DataFrame.from_dict(out, orient='index')
return df.sort_values(by=0).iloc[0, 1]