Feols.ritest

ritest(
    resampvar,
    cluster=None,
    reps=100,
    type='randomization-c',
    rng=None,
    choose_algorithm='auto',
    store_ritest_statistics=False,
    level=0.95,
)

Conduct Randomization Inference (RI) test against a null hypothesis of resampvar = 0.

Parameters

Name	Type	Description	Default
resampvar	str	The name of the variable to be resampled.	required
cluster	str	The name of the cluster variable in case of cluster random assignment. If provided, `resampvar` is held constant within each `cluster`. Defaults to None.	`None`
reps	int	The number of randomization iterations. Defaults to 100.	`100`
type	str	The type of the randomization inference test. Can be “randomization-c” or “randomization-t”. Note that the “randomization-c” is much faster, while the “randomization-t” is recommended by Wu & Ding (JASA, 2021).	`'randomization-c'`
rng	np.random.Generator	A random number generator. Defaults to None.	`None`
choose_algorithm	str	The algorithm to use for the computation. Defaults to “auto”. The alternatives are “fast” and “slow”. The fast algorithm requires the optional `numba` extra (install via `pip install pyfixest[numba]`); without it, the fast path raises an `ImportError`. The slow path does not require numba.	`'auto'`
include_plot		Whether to include a plot of the distribution p-values. Defaults to False.	required
store_ritest_statistics	bool	Whether to store the simulated statistics of the RI procedure. Defaults to False. If True, stores the simulated statistics in the model object via the `ritest_statistics` attribute as a numpy array.	`False`
level	float	The level for the confidence interval of the randomization inference p-value. Defaults to 0.95.	`0.95`

Returns

Name	Type	Description
	A pd.Series with the regression coefficient of `resampvar` and the p-value
	of the RI test. Additionally, reports the standard error and the confidence
	interval of the p-value.

Examples

import pyfixest as pf
data = pf.get_data()
fit = pf.feols("Y ~ X1 + X2", data=data)

# Conduct a randomization inference test for the coefficient of X1
fit.ritest("X1", reps=1000)

# use randomization-t instead of randomization-c
fit.ritest("X1", reps=1000, type="randomization-t")

# store statistics for plotting
fit.ritest("X1", reps=1000, store_ritest_statistics=True)

H0                                      X1=0
ri-type                      randomization-c
Estimate                 -0.9929357698186863
Pr(>|t|)                                 0.0
Std. Error (Pr(>|t|))                    0.0
2.5% (Pr(>|t|))                          0.0
97.5% (Pr(>|t|))                         0.0
dtype: object