Feols.wildboottest

wildboottest(
    reps,
    cluster=None,
    param=None,
    weights_type='rademacher',
    impose_null=True,
    bootstrap_type='11',
    seed=None,
    k_adj=True,
    G_adj=True,
    parallel=False,
    return_bootstrapped_t_stats=False,
)

Run a wild cluster bootstrap based on an object of type “Feols”.

Parameters

Name	Type	Description	Default
reps	int	The number of bootstrap iterations to run.	required
cluster	Union[str, None]	The variable used for clustering. Defaults to None. If None, then uses the variable specified in the model’s `clustervar` attribute. If no `_clustervar` attribute is found, runs a heteroskedasticity- robust bootstrap.	`None`
param	Union[str, None]	A string of length one, containing the test parameter of interest. Defaults to None.	`None`
weights_type	str	The type of bootstrap weights. Options are ‘rademacher’, ‘mammen’, ‘webb’, or ‘normal’. Defaults to ‘rademacher’.	`'rademacher'`
impose_null	bool	Indicates whether to impose the null hypothesis on the bootstrap DGP. Defaults to True.	`True`
bootstrap_type	str	A string of length one to choose the bootstrap type. Options are ‘11’, ‘31’, ‘13’, or ‘33’. Defaults to ‘11’.	`'11'`
seed	Union[int, None]	An option to provide a random seed. Defaults to None.	`None`
k_adj	bool	Indicates whether to apply a small sample adjustment for the number of observations and covariates. Defaults to True.	`True`
G_adj	bool	Indicates whether to apply a small sample adjustment for the number of clusters. Defaults to True.	`True`
parallel	bool	Indicates whether to run the bootstrap in parallel. Defaults to False.	`False`
seed	Union[str, None]	An option to provide a random seed. Defaults to None.	`None`
return_bootstrapped_t_stats	bool, optional:	If True, the method returns a tuple of the regular output and the bootstrapped t-stats. Defaults to False.	`False`

Returns

Name	Type	Description
	pd.DataFrame	A DataFrame with the original, non-bootstrapped t-statistic and bootstrapped p-value, along with the bootstrap type, inference type (HC vs CRV), and whether the null hypothesis was imposed on the bootstrap DGP. If `return_bootstrapped_t_stats` is True, the method returns a tuple of the regular output and the bootstrapped t-stats.

Examples

import re
import pyfixest as pf

data = pf.get_data()
fit = pf.feols("Y ~ X1 + X2 | f1", data)

fit.wildboottest(
    param = "X1",
    reps=1000,
    seed = 822
)

fit.wildboottest(
    param = "X1",
    reps=1000,
    seed = 822,
    bootstrap_type = "31"
)

param                    X1
t value          -14.843741
Pr(>|t|)                0.0
bootstrap_type           31
inference                HC
impose_null            True
ssc                       1
dtype: object