estimation.feols_compressed_.FeolsCompressed

estimation.feols_compressed_.FeolsCompressed(
    self
    FixestFormula
    data
    ssc_dict
    drop_singletons
    drop_intercept
    weights
    weights_type
    collin_tol
    fixef_tol
    lookup_demeaned_data
    solver='np.linalg.solve'
    store_data=True
    copy_data=True
    lean=False
    reps=100
    seed=None
    sample_split_var=None
    sample_split_value=None
)

Non-user-facing class for compressed regression with fixed effects.

See the paper “You only compress once” by Wong et al (https://arxiv.org/abs/2102.11297) for details on regression compression.

Parameters

Name	Type	Description	Default
FixestFormula	FixestFormula	The formula object.	required
data	pd.DataFrame	The data.	required
ssc_dict	dict[str, Union[str, bool]]	The ssc dictionary.	required
drop_singletons	bool	Whether to drop columns with singleton fixed effects.	required
drop_intercept	bool	Whether to include an intercept.	required
weights	Optional[str]	The column name of the weights. None if no weights are used. For this method, weights needs to be None.	required
weights_type	Optional[str]	The type of weights. For this method, weights_type needs to be ‘fweights’.	required
collin_tol	float	The tolerance level for collinearity.	required
fixef_tol	float	The tolerance level for the fixed effects.	required
lookup_demeaned_data	dict[str, pd.DataFrame]	The lookup table for demeaned data.	required
solver	str	The solver to use.	'np.linalg.solve'
store_data	bool	Whether to store the data.	True
copy_data	bool	Whether to copy the data.	True
lean	bool	Whether to keep memory-heavy objects as attributes or not.	False
reps	int	The number of bootstrap repetitions. Default is 100. Only used for CRV1 inference, where a wild cluster bootstrap is used.	100
seed	Optional[int]	The seed for the random number generator. Only relevant for CRV1 inference, where a wild cluster bootstrap is used.	None

Methods

Name	Description
predict	Compute predicted values.
prepare_model_matrix	Prepare model inputs for estimation.
vcov	Compute the variance-covariance matrix for the compressed regression.

predict

estimation.feols_compressed_.FeolsCompressed.predict(
    newdata=None
    atol=1e-06
    btol=1e-06
    type='link'
)

Compute predicted values.

Parameters

Name	Type	Description	Default
newdata	Optional[DataFrameType]	The new data. If None, makes a prediction based on the uncompressed data set.	None
atol	float	The absolute tolerance.	1e-06
btol	float	The relative tolerance.	1e-06
type	str	The type of prediction.	'link'

Returns

Name	Type	Description
	np.ndarray	The predicted values. If newdata is None, the predicted values are based on the uncompressed data set.

prepare_model_matrix

estimation.feols_compressed_.FeolsCompressed.prepare_model_matrix()

Prepare model inputs for estimation.

vcov

estimation.feols_compressed_.FeolsCompressed.vcov(vcov, data=None)

Compute the variance-covariance matrix for the compressed regression.