def fit(
self,
*,
cov_type: str = "robust",
debiased: bool = False,
method: str = "auto",
absorb_options: Optional[Dict[str, Union[bool, str, ArrayLike, None,
Dict[str, Any]]]] = None,
use_cache: bool = True,
lsmr_options: Optional[Dict[str, Union[float, bool]]] = None,
**cov_config: Any,
) -> AbsorbingLSResults:
"""
Estimate model parameters
Parameters
----------
cov_type : str, optional
Name of covariance estimator to use. Supported covariance
estimators are:
* 'unadjusted', 'homoskedastic' - Classic homoskedastic inference
* 'robust', 'heteroskedastic' - Heteroskedasticity robust inference
* 'kernel' - Heteroskedasticity and autocorrelation robust
inference
* 'cluster' - One-way cluster dependent inference.
Heteroskedasticity robust
debiased : bool, optional
Flag indicating whether to debiased the covariance estimator using
a degree of freedom adjustment.
method : str, optional
One of:
* "auto" - (Default). Use HDFE when applicable and fallback to LSMR.
* "lsmr" - Force LSMR.
* "hdfe" - Force HDFE. Raises RuntimeError if the model contains
continuous variables or continuous-binary interactions to absorb or
if the model is weighted.
absorb_options : dict, optional
Dictionary of options to pass to the absorber. Passed to either
scipy.sparse.linalg.lsmr or pyhdfe.create depending on the method used
to absorb the absorbed regressors.
use_cache : bool
Flag indicating whether the variables, once purged from the
absorbed variables and interactions, should be stored in the cache,
and retrieved if available. Cache can dramatically speed up
re-fitting large models when the set of absorbed variables and
interactions are identical.
lsmr_options: dict
Options to ass to scipy.sparse.linalg.lsmr.
.. deprecated:: 4.17
Use absorb_options to pass options
**cov_config
Additional parameters to pass to covariance estimator. The list
of optional parameters differ according to ``cov_type``. See
the documentation of the alternative covariance estimators for
the complete list of available commands.
Returns
-------
AbsorbingLSResults
Results container
Notes
-----
Additional covariance parameters depend on specific covariance used.
The see the docstring of specific covariance estimator for a list of
supported options. Defaults are used if no covariance configuration
is provided.
If use_cache is True, then variables are hashed based on their
contents using either a 64 bit value (if xxhash is installed) or
a 256 bit value. This allows variables to be reused in different
models if the set of absorbing variables and interactions is held
constant.
See also
--------
linearmodels.iv.covariance.HomoskedasticCovariance
linearmodels.iv.covariance.HeteroskedasticCovariance
linearmodels.iv.covariance.KernelCovariance
linearmodels.iv.covariance.ClusteredCovariance
"""
if lsmr_options is not None:
if absorb_options is not None:
raise ValueError(
"absorb_options cannot be used with lsmr_options")
warnings.warn("lsmr_options is deprecated. Use absorb_options.",
FutureWarning)
absorb_options = {k: v for k, v in lsmr_options.items()}
if self._absorbed_dependent is None:
self._first_time_fit(use_cache, absorb_options, method)
exog_resid = self.absorbed_exog.to_numpy()
dep_resid = self.absorbed_dependent.to_numpy()
if self._exog.shape[1] == 0:
params = empty((0, 1))
else:
params = lstsq(exog_resid, dep_resid, rcond=None)[0]
self._num_params += exog_resid.shape[1]
cov_estimator = COVARIANCE_ESTIMATORS[cov_type]
cov_config["debiased"] = debiased
cov_config["kappa"] = 0.0
cov_config_copy = {k: v for k, v in cov_config.items()}
if "center" in cov_config_copy:
del cov_config_copy["center"]
cov_estimator_inst = cov_estimator(exog_resid, dep_resid, exog_resid,
params, **cov_config_copy)
results = {"kappa": 0.0, "liml_kappa": 0.0}
pe = self._post_estimation(params, cov_estimator_inst, cov_type)
results.update(pe)
results["df_model"] = self._num_params
return AbsorbingLSResults(results, self)
def fit(
self,
*,
cov_type: str = "robust",
debiased: bool = False,
lsmr_options: Optional[Dict[str, Union[float, bool]]] = None,
use_cache: bool = True,
**cov_config: Any,
) -> AbsorbingLSResults:
"""
Estimate model parameters
Parameters
----------
cov_type : str, optional
Name of covariance estimator to use. Supported covariance
estimators are:
* 'unadjusted', 'homoskedastic' - Classic homoskedastic inference
* 'robust', 'heteroskedastic' - Heteroskedasticity robust inference
* 'kernel' - Heteroskedasticity and autocorrelation robust
inference
* 'cluster' - One-way cluster dependent inference.
Heteroskedasticity robust
debiased : bool, optional
Flag indicating whether to debiased the covariance estimator using
a degree of freedom adjustment.
**cov_config
Additional parameters to pass to covariance estimator. The list
of optional parameters differ according to ``cov_type``. See
the documentation of the alternative covariance estimators for
the complete list of available commands.
lsmr_options : dict
Dictionary of options to pass to scipy.sparse.linalg.lsmr
use_cache : bool
Flag indicating whether the variables, once purged from the
absorbed variables and interactions, should be stored in the cache,
and retrieved if available. Cache can dramatically speed up
re-fitting large models when the set of absorbed variables and
interactions are identical.
Returns
-------
AbsorbingLSResults
Results container
Notes
-----
Additional covariance parameters depend on specific covariance used.
The see the docstring of specific covariance estimator for a list of
supported options. Defaults are used if no covariance configuration
is provided.
If use_cache is True, then variables are hashed based on their
contents using either a 64 bit value (if xxhash is installed) or
a 256 bit value. This allows variables to be reused in different
models if the set of absorbing variables and interactions is held
constant.
See also
--------
linearmodels.iv.covariance.HomoskedasticCovariance
linearmodels.iv.covariance.HeteroskedasticCovariance
linearmodels.iv.covariance.KernelCovariance
linearmodels.iv.covariance.ClusteredCovariance
"""
if self._absorbed_dependent is None:
self._first_time_fit(use_cache, lsmr_options)
self._x = exog_resid = to_numpy(self.absorbed_exog)
dep_resid = to_numpy(self.absorbed_dependent)
if self._exog.shape[1] == 0:
params = empty((0, 1))
else:
if exog_resid.shape[1]:
check_absorbed(exog_resid, self.exog.cols)
params = lstsq(exog_resid, dep_resid, rcond=None)[0]
self._num_params += exog_resid.shape[1]
cov_estimator = COVARIANCE_ESTIMATORS[cov_type]
cov_config["debiased"] = debiased
cov_config["kappa"] = 0.0
cov_config_copy = {k: v for k, v in cov_config.items()}
if "center" in cov_config_copy:
del cov_config_copy["center"]
cov_estimator_inst = cov_estimator(exog_resid, dep_resid, exog_resid,
params, **cov_config_copy)
results = {"kappa": 0.0, "liml_kappa": 0.0}
pe = self._post_estimation(params, cov_estimator_inst, cov_type)
results.update(pe)
results["df_model"] = self._num_params
return AbsorbingLSResults(results, self)