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
from pyfixest.estimation import feols
from pyfixest.utils import get_data
from pyfixest.multcomp import rwolf
data = get_data().dropna()
fit = feols("Y ~ Y2 + X1 + X2", data=data)
rwolf(fit.to_list(), "X1", reps=9999, seed=123)
fit1 = feols("Y ~ X1", data=data)
fit2 = feols("Y ~ X1 + X2", data=data)
rwolf_df = rwolf([fit1, fit2], "X1", reps=9999, seed=123)
rwolf_df