def test_panel_robust_cov():
import pandas as pa
import statsmodels.datasets.grunfeld as gr
from .results.results_panelrobust import results as res_stata
dtapa = gr.data.load_pandas()
#Stata example/data seems to miss last firm
dtapa_endog = dtapa.endog[:200]
dtapa_exog = dtapa.exog[:200]
res = OLS(dtapa_endog,
add_constant(dtapa_exog[['value', 'capital']],
prepend=False)).fit()
#time indicator in range(max Ti)
time = np.asarray(dtapa_exog[['year']])
time -= time.min()
time = np.squeeze(time).astype(int)
#sw.cov_nw_panel requires bounds instead of index
tidx = [(i * 20, 20 * (i + 1)) for i in range(10)]
#firm index in range(n_firms)
firm_names, firm_id = np.unique(np.asarray(dtapa_exog[['firm']], 'S20'),
return_inverse=True)
#panel newey west standard errors
cov = sw.cov_nw_panel(res, 0, tidx, use_correction='hac')
#dropping numpy 1.4 soon
#np.testing.assert_allclose(cov, res_stata.cov_pnw0_stata, rtol=1e-6)
assert_almost_equal(cov, res_stata.cov_pnw0_stata, decimal=4)
cov = sw.cov_nw_panel(res, 1, tidx, use_correction='hac')
#np.testing.assert_allclose(cov, res_stata.cov_pnw1_stata, rtol=1e-6)
assert_almost_equal(cov, res_stata.cov_pnw1_stata, decimal=4)
cov = sw.cov_nw_panel(res, 4, tidx) #check default
#np.testing.assert_allclose(cov, res_stata.cov_pnw4_stata, rtol=1e-6)
assert_almost_equal(cov, res_stata.cov_pnw4_stata, decimal=4)
#cluster robust standard errors
cov_clu = sw.cov_cluster(res, firm_id)
assert_almost_equal(cov_clu, res_stata.cov_clu_stata, decimal=4)
#cluster robust standard errors, non-int groups
cov_clu = sw.cov_cluster(res, lmap(str, firm_id))
assert_almost_equal(cov_clu, res_stata.cov_clu_stata, decimal=4)
#Driscoll and Kraay panel robust standard errors
rcov = sw.cov_nw_groupsum(res, 0, time, use_correction=0)
assert_almost_equal(rcov, res_stata.cov_dk0_stata, decimal=4)
rcov = sw.cov_nw_groupsum(res, 1, time, use_correction=0)
assert_almost_equal(rcov, res_stata.cov_dk1_stata, decimal=4)
rcov = sw.cov_nw_groupsum(res, 4, time) #check default
assert_almost_equal(rcov, res_stata.cov_dk4_stata, decimal=4)
def test_panel_robust_cov():
import pandas as pa
import statsmodels.datasets.grunfeld as gr
from results.results_panelrobust import results as res_stata
dtapa = gr.data.load_pandas()
#Stata example/data seems to miss last firm
dtapa_endog = dtapa.endog[:200]
dtapa_exog = dtapa.exog[:200]
res = OLS(dtapa_endog, add_constant(dtapa_exog[['value', 'capital']],
prepend=False)).fit()
#time indicator in range(max Ti)
time = np.asarray(dtapa_exog[['year']])
time -= time.min()
time = np.squeeze(time).astype(int)
#sw.cov_nw_panel requires bounds instead of index
tidx = [(i*20, 20*(i+1)) for i in range(10)]
#firm index in range(n_firms)
firm_names, firm_id = np.unique(np.asarray(dtapa_exog[['firm']], 'S20'),
return_inverse=True)
#panel newey west standard errors
cov = sw.cov_nw_panel(res, 0, tidx, use_correction='hac')
#dropping numpy 1.4 soon
#np.testing.assert_allclose(cov, res_stata.cov_pnw0_stata, rtol=1e-6)
assert_almost_equal(cov, res_stata.cov_pnw0_stata, decimal=4)
cov = sw.cov_nw_panel(res, 1, tidx, use_correction='hac')
#np.testing.assert_allclose(cov, res_stata.cov_pnw1_stata, rtol=1e-6)
assert_almost_equal(cov, res_stata.cov_pnw1_stata, decimal=4)
cov = sw.cov_nw_panel(res, 4, tidx) #check default
#np.testing.assert_allclose(cov, res_stata.cov_pnw4_stata, rtol=1e-6)
assert_almost_equal(cov, res_stata.cov_pnw4_stata, decimal=4)
#cluster robust standard errors
cov_clu = sw.cov_cluster(res, firm_id)
assert_almost_equal(cov_clu, res_stata.cov_clu_stata, decimal=4)
#cluster robust standard errors, non-int groups
cov_clu = sw.cov_cluster(res, map(str, firm_id))
assert_almost_equal(cov_clu, res_stata.cov_clu_stata, decimal=4)
#Driscoll and Kraay panel robust standard errors
rcov = sw.cov_nw_groupsum(res, 0, time, use_correction=0)
assert_almost_equal(rcov, res_stata.cov_dk0_stata, decimal=4)
rcov = sw.cov_nw_groupsum(res, 1, time, use_correction=0)
assert_almost_equal(rcov, res_stata.cov_dk1_stata, decimal=4)
rcov = sw.cov_nw_groupsum(res, 4, time) #check default
assert_almost_equal(rcov, res_stata.cov_dk4_stata, decimal=4)
def setup(self):
res_ols = self.res1.model.fit(
cov_type="nw-groupsum",
cov_kwds=dict(time=self.time,
maxlags=4,
use_correction=False,
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_nw_groupsum(self.res1,
4,
self.time,
use_correction=False)
se1 = sw.se_cov(cov1)
self.bse_robust2 = se1
self.cov_robust2 = cov1
self.small = True
self.res2 = res2.results_nw_groupsum4
self.skip_f = True
self.rtol = 1e-6
self.rtolh = 1e-10
def setup(self):
res_ols = self.res1.model.fit(
cov_type="nw-groupsum", cov_kwds=dict(time=self.time, maxlags=4, use_correction=False, 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_nw_groupsum(self.res1, 4, self.time, use_correction=False)
se1 = sw.se_cov(cov1)
self.bse_robust2 = se1
self.cov_robust2 = cov1
self.small = True
self.res2 = res2.results_nw_groupsum4
self.skip_f = True
self.rtol = 1e-6
self.rtolh = 1e-10
def setup(self):
res_ols = self.res1.get_robustcov_results('nw-groupsum',
time=self.time,
maxlags=4,
use_correction=False,
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_nw_groupsum(self.res1, 4, self.time, use_correction=False)
se1 = sw.se_cov(cov1)
self.bse_robust2 = se1
self.cov_robust2 = cov1
self.small = True
self.res2 = res2.results_nw_groupsum4
self.skip_f = True
self.rtol = 1e-6
self.rtolh = 1e-10
def get_robustcov_results(self, cov_type='HC1', use_t=None, **kwds):
"""create new results instance with robust covariance as default
Parameters
----------
cov_type : string
the type of robust sandwich estimator to use. see Notes below
use_t : bool
If true, then the t distribution is used for inference.
If false, then the normal distribution is used.
kwds : depends on cov_type
Required or optional arguments for robust covariance calculation.
see Notes below
Returns
-------
results : results instance
This method creates a new results instance with the requested
robust covariance as the default covariance of the parameters.
Inferential statistics like p-values and hypothesis tests will be
based on this covariance matrix.
Notes
-----
Warning: Some of the options and defaults in cov_kwds may be changed in a
future version.
The covariance keywords provide an option 'scaling_factor' to adjust the
scaling of the covariance matrix, that is the covariance is multiplied by
this factor if it is given and is not `None`. This allows the user to
adjust the scaling of the covariance matrix to match other statistical
packages.
For example, `scaling_factor=(nobs - 1.) / (nobs - k_params)` provides a
correction so that the robust covariance matrices match those of Stata in
some models like GLM and discrete Models.
The following covariance types and required or optional arguments are
currently available:
- 'HC0', 'HC1', 'HC2', 'HC3' and no keyword arguments:
heteroscedasticity robust covariance
- 'HAC' and keywords
- `maxlag` integer (required) : number of lags to use
- `kernel` string (optional) : kernel, default is Bartlett
- `use_correction` bool (optional) : If true, use small sample
correction
- 'cluster' and required keyword `groups`, integer group indicator
- `groups` array_like, integer (required) :
index of clusters or groups
- `use_correction` bool (optional) :
If True the sandwich covariance is calulated with a small
sample correction.
If False the the sandwich covariance is calulated without
small sample correction.
- `df_correction` bool (optional)
If True (default), then the degrees of freedom for the
inferential statistics and hypothesis tests, such as
pvalues, f_pvalue, conf_int, and t_test and f_test, are
based on the number of groups minus one instead of the
total number of observations minus the number of explanatory
variables. `df_resid` of the results instance is adjusted.
If False, then `df_resid` of the results instance is not
adjusted.
- 'hac-groupsum' Driscoll and Kraay, heteroscedasticity and
autocorrelation robust standard errors in panel data
keywords
- `time` array_like (required) : index of time periods
- `maxlag` integer (required) : number of lags to use
- `kernel` string (optional) : kernel, default is Bartlett
- `use_correction` False or string in ['hac', 'cluster'] (optional) :
If False the the sandwich covariance is calulated without
small sample correction.
If `use_correction = 'cluster'` (default), then the same
small sample correction as in the case of 'covtype='cluster''
is used.
- `df_correction` bool (optional)
adjustment to df_resid, see cov_type 'cluster' above
#TODO: we need more options here
- 'hac-panel' heteroscedasticity and autocorrelation robust standard
errors in panel data.
The data needs to be sorted in this case, the time series for
each panel unit or cluster need to be stacked. The membership to
a timeseries of an individual or group can be either specified by
group indicators or by increasing time periods.
keywords
- either `groups` or `time` : array_like (required)
`groups` : indicator for groups
`time` : index of time periods
- `maxlag` integer (required) : number of lags to use
- `kernel` string (optional) : kernel, default is Bartlett
- `use_correction` False or string in ['hac', 'cluster'] (optional) :
If False the the sandwich covariance is calulated without
small sample correction.
- `df_correction` bool (optional)
adjustment to df_resid, see cov_type 'cluster' above
#TODO: we need more options here
Reminder:
`use_correction` in "hac-groupsum" and "hac-panel" is not bool,
needs to be in [False, 'hac', 'cluster']
TODO: Currently there is no check for extra or misspelled keywords,
except in the case of cov_type `HCx`
"""
import statsmodels.stats.sandwich_covariance as sw
#normalize names
if cov_type == 'nw-panel':
cov_type = 'hac-panel'
if cov_type == 'nw-groupsum':
cov_type = 'hac-groupsum'
if 'kernel' in kwds:
kwds['weights_func'] = kwds.pop('kernel')
# pop because HCx raises if any kwds
sc_factor = kwds.pop('scaling_factor', None)
# TODO: make separate function that returns a robust cov plus info
use_self = kwds.pop('use_self', False)
if use_self:
res = self
else:
# this doesn't work for most models, use raw instance instead from fit
res = self.__class__(self.model, self.params,
normalized_cov_params=self.normalized_cov_params,
scale=self.scale)
res.cov_type = cov_type
# use_t might already be defined by the class, and already set
if use_t is None:
use_t = self.use_t
res.cov_kwds = {'use_t':use_t} # store for information
res.use_t = use_t
adjust_df = False
if cov_type in ['cluster', 'hac-panel', 'hac-groupsum']:
df_correction = kwds.get('df_correction', None)
# TODO: check also use_correction, do I need all combinations?
if df_correction is not False: # i.e. in [None, True]:
# user didn't explicitely set it to False
adjust_df = True
res.cov_kwds['adjust_df'] = adjust_df
# verify and set kwds, and calculate cov
# TODO: this should be outsourced in a function so we can reuse it in
# other models
# TODO: make it DRYer repeated code for checking kwds
if cov_type.upper() in ('HC0', 'HC1', 'HC2', 'HC3'):
if kwds:
raise ValueError('heteroscedasticity robust covarians ' +
'does not use keywords')
res.cov_kwds['description'] = ('Standard Errors are heteroscedasticity ' +
'robust ' + '(' + cov_type + ')')
res.cov_params_default = getattr(self, 'cov_' + cov_type.upper(), None)
if res.cov_params_default is None:
# results classes that don't have cov_HCx attribute
res.cov_params_default = sw.cov_white_simple(self,
use_correction=False)
elif cov_type.lower() == 'hac':
maxlags = kwds['maxlags'] # required?, default in cov_hac_simple
res.cov_kwds['maxlags'] = maxlags
weights_func = kwds.get('weights_func', sw.weights_bartlett)
res.cov_kwds['weights_func'] = weights_func
use_correction = kwds.get('use_correction', False)
res.cov_kwds['use_correction'] = use_correction
res.cov_kwds['description'] = ('Standard Errors are heteroscedasticity ' +
'and autocorrelation robust (HAC) using %d lags and %s small ' +
'sample correction') % (maxlags, ['without', 'with'][use_correction])
res.cov_params_default = sw.cov_hac_simple(self, nlags=maxlags,
weights_func=weights_func,
use_correction=use_correction)
elif cov_type.lower() == 'cluster':
#cluster robust standard errors, one- or two-way
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))
res.cov_params_default = sw.cov_cluster(self, groups,
use_correction=use_correction)
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
res.cov_params_default = sw.cov_cluster_2groups(self, groups,
use_correction=use_correction)[0]
else:
raise ValueError('only two groups are supported')
res.cov_kwds['description'] = ('Standard Errors are robust to' +
'cluster correlation ' + '(' + cov_type + ')')
elif cov_type.lower() == 'hac-panel':
#cluster robust standard errors
res.cov_kwds['time'] = time = kwds.get('time', None)
res.cov_kwds['groups'] = groups = kwds.get('groups', None)
#TODO: nlags is currently required
#nlags = kwds.get('nlags', True)
#res.cov_kwds['nlags'] = nlags
#TODO: `nlags` or `maxlags`
res.cov_kwds['maxlags'] = maxlags = kwds['maxlags']
use_correction = kwds.get('use_correction', 'hac')
res.cov_kwds['use_correction'] = use_correction
weights_func = kwds.get('weights_func', sw.weights_bartlett)
res.cov_kwds['weights_func'] = weights_func
# TODO: clumsy time index in cov_nw_panel
if groups is not None:
groups = np.asarray(groups)
tt = (np.nonzero(groups[:-1] != groups[1:])[0] + 1).tolist()
nobs_ = len(groups)
elif time is not None:
# TODO: clumsy time index in cov_nw_panel
time = np.asarray(time)
tt = (np.nonzero(time[1:] < time[:-1])[0] + 1).tolist()
nobs_ = len(time)
else:
raise ValueError('either time or groups needs to be given')
groupidx = lzip([0] + tt, tt + [nobs_])
self.n_groups = n_groups = len(groupidx)
res.cov_params_default = sw.cov_nw_panel(self, maxlags, groupidx,
weights_func=weights_func,
use_correction=use_correction)
res.cov_kwds['description'] = ('Standard Errors are robust to' +
'cluster correlation ' + '(' + cov_type + ')')
elif cov_type.lower() == 'hac-groupsum':
# Driscoll-Kraay standard errors
res.cov_kwds['time'] = time = kwds['time']
#TODO: nlags is currently required
#nlags = kwds.get('nlags', True)
#res.cov_kwds['nlags'] = nlags
#TODO: `nlags` or `maxlags`
res.cov_kwds['maxlags'] = maxlags = kwds['maxlags']
use_correction = kwds.get('use_correction', 'cluster')
res.cov_kwds['use_correction'] = use_correction
weights_func = kwds.get('weights_func', sw.weights_bartlett)
res.cov_kwds['weights_func'] = weights_func
if adjust_df:
# need to find number of groups
tt = (np.nonzero(time[1:] < time[:-1])[0] + 1)
self.n_groups = n_groups = len(tt) + 1
res.cov_params_default = sw.cov_nw_groupsum(self, maxlags, time,
weights_func=weights_func,
use_correction=use_correction)
res.cov_kwds['description'] = (
'Driscoll and Kraay Standard Errors are robust to ' +
'cluster correlation ' + '(' + cov_type + ')')
else:
raise ValueError('cov_type not recognized. See docstring for ' +
'available options and spelling')
# generic optional factor to scale covariance
res.cov_kwds['scaling_factor'] = sc_factor
if sc_factor is not None:
res.cov_params_default *= sc_factor
if adjust_df:
# Note: df_resid is used for scale and others, add new attribute
res.df_resid_inference = n_groups - 1
return res