estimation.rwolf

estimation.rwolf(models, param, reps, seed, sampling_method='wild-bootstrap')

Compute Romano-Wolf adjusted p-values for multiple hypothesis testing.

For each model, it is assumed that tests to adjust are of the form “param = 0”. This function uses the wildboottest() method for running the bootstrap, hence models of type Feiv or Fepois are not supported.

Parameters

Name	Type	Description	Default
models	list[Feols] or FixestMulti	A list of models for which the p-values should be computed, or a FixestMulti object. Models of type `Feiv` or `Fepois` are not supported.	required
param	str	The parameter for which the p-values should be computed.	required
reps	int	The number of bootstrap replications.	required
seed	int	The seed for the random number generator.	required
sampling_method	str	Sampling method for computing resampled statistics. Users can choose either bootstrap(‘wild-bootstrap’) or randomization inference(‘ri’)	`'wild-bootstrap'`

Returns

Name	Type	Description
	pd.DataFrame	A DataFrame containing estimation statistics, including the Romano-Wolf adjusted p-values.

Examples

import pyfixest as pf
from pyfixest.utils import get_data

data = get_data().dropna()
fit = pf.feols("Y ~ Y2 + X1 + X2", data=data)
pf.rwolf(fit, "X1", reps=9999, seed=123)

fit1 = pf.feols("Y ~ X1", data=data)
fit2 = pf.feols("Y ~ X1 + X2", data=data)
rwolf_df = pf.rwolf([fit1, fit2], "X1", reps=9999, seed=123)

# use randomization inference - dontrun as too slow
# rwolf_df = pf.rwolf([fit1, fit2], "X1", reps=9999, seed=123, sampling_method = "ri")

rwolf_df

	est0	est1
Estimate	-1.001930	-0.995197
Std. Error	0.084823	0.082194
t value	-11.811964	-12.107957
Pr(>\|t\|)	0.000000	0.000000
2.5%	-1.168383	-1.156490
97.5%	-0.835476	-0.833904
RW Pr(>\|t\|)	0.000100	0.000100