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.