linearmodels.iv.absorbing.AbsorbingLS.fit¶
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AbsorbingLS.fit(*, cov_type=
'robust', debiased=False, method='auto', absorb_options=None, use_cache=True, lsmr_options=None, **cov_config)[source]¶ Estimate model parameters
- Parameters:¶
- cov_type : str¶
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¶
Flag indicating whether to debiased the covariance estimator using a degree of freedom adjustment.
- method : str¶
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¶
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 since version 4.17: Use absorb_options to pass options
- **cov_config :
Any 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:¶
Results container
- Return type:¶
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.