def setup(self):
res_ols = self.res1.model.fit(cov_type='cluster',
cov_kwds=dict(groups=self.groups,
use_correction=True,
use_t=True))
self.res3 = self.res1
self.res1 = res_ols
self.bse_robust = res_ols.bse
self.cov_robust = res_ols.cov_params()
cov1 = sw.cov_cluster(self.res1, self.groups, use_correction=True)
se1 = sw.se_cov(cov1)
self.bse_robust2 = se1
self.cov_robust2 = cov1
def setup(self):
res_ols = self.res1.get_robustcov_results('cluster',
groups=self.groups,
use_correction=True,
use_t=True)
self.res3 = self.res1
self.res1 = res_ols
self.bse_robust = res_ols.bse
self.cov_robust = res_ols.cov_params()
cov1 = sw.cov_cluster(self.res1, self.groups, use_correction=True)
se1 = sw.se_cov(cov1)
self.bse_robust2 = se1
self.cov_robust2 = cov1
def setup(self):
fat_array = self.groups.reshape(-1, 1)
fat_groups = pd.DataFrame(fat_array)
res_ols = self.res1.get_robustcov_results('cluster',
groups=fat_groups,
use_correction=True,
use_t=True)
self.res3 = self.res1
self.res1 = res_ols
self.bse_robust = res_ols.bse
self.cov_robust = res_ols.cov_params()
cov1 = sw.cov_cluster(self.res1, self.groups, use_correction=True)
se1 = sw.se_cov(cov1)
self.bse_robust2 = se1
self.cov_robust2 = cov1
def clustered_stderrs(self, res, kwds, cov_type, adjust_df):
# cluster robust standard errors, one- or two-way
n_groups = None
groups = kwds['groups']
if not hasattr(groups, 'shape'):
groups = np.asarray(groups).T
if groups.ndim >= 2:
groups = groups.squeeze()
res.cov_kwds['groups'] = groups
use_correction = kwds.get('use_correction', True)
res.cov_kwds['use_correction'] = use_correction
if groups.ndim == 1:
if adjust_df:
# need to find number of groups
# duplicate work
self.n_groups = n_groups = len(np.unique(groups))
cpd = sw.cov_cluster(self, groups, use_correction=use_correction)
res.cov_params_default = cpd
elif groups.ndim == 2:
if hasattr(groups, 'values'):
groups = groups.values
if adjust_df:
# need to find number of groups
# duplicate work
n_groups0 = len(np.unique(groups[:, 0]))
n_groups1 = len(np.unique(groups[:, 1]))
self.n_groups = (n_groups0, n_groups1)
n_groups = min(n_groups0, n_groups1) # use for adjust_df
# Note: sw.cov_cluster_2groups has 3 returns
cpd = sw.cov_cluster_2groups(self, groups,
use_correction=use_correction)[0]
res.cov_params_default = cpd
else: # pragma: no cover
raise ValueError('only two groups are supported')
res.cov_kwds['description'] = ('Standard Errors are robust to '
'cluster correlation ({cov_type})'
.format(cov_type=cov_type))
return n_groups
def get_robust_clu(cls):
res1 = cls.res1
cov_clu = sw.cov_cluster(res1, group)
cls.bse_rob = sw.se_cov(cov_clu)
cls.get_corr_fact()