diff_diff.SABootstrapResults

class diff_diff.SABootstrapResults[source]

Bases: object

Results from Sun-Abraham bootstrap inference.

n_bootstrap

Number of bootstrap iterations.

Type:

int

weight_type

Type of bootstrap used (always “pairs” for pairs bootstrap).

Type:

str

alpha

Significance level used for confidence intervals.

Type:

float

overall_att_se

Bootstrap standard error for overall ATT.

Type:

float

overall_att_ci

Bootstrap confidence interval for overall ATT.

Type:

Tuple[float, float]

overall_att_p_value

Bootstrap p-value for overall ATT.

Type:

float

event_study_ses

Bootstrap SEs for event study effects.

Type:

Dict[int, float]

event_study_cis

Bootstrap CIs for event study effects.

Type:

Dict[int, Tuple[float, float]]

event_study_p_values

Bootstrap p-values for event study effects.

Type:

Dict[int, float]

bootstrap_distribution

Full bootstrap distribution of overall ATT.

Type:

Optional[np.ndarray]

__init__(n_bootstrap, weight_type, alpha, overall_att_se, overall_att_ci, overall_att_p_value, event_study_ses, event_study_cis, event_study_p_values, bootstrap_distribution=None)
Parameters:
Return type:

None

Methods

__init__(n_bootstrap, weight_type, alpha, ...)

Attributes

bootstrap_distribution

n_bootstrap

weight_type

alpha

overall_att_se

overall_att_ci

overall_att_p_value

event_study_ses

event_study_cis

event_study_p_values

n_bootstrap: int
weight_type: str
alpha: float
overall_att_se: float
overall_att_ci: Tuple[float, float]
overall_att_p_value: float
event_study_ses: Dict[int, float]
event_study_cis: Dict[int, Tuple[float, float]]
event_study_p_values: Dict[int, float]
bootstrap_distribution: ndarray | None = None
__init__(n_bootstrap, weight_type, alpha, overall_att_se, overall_att_ci, overall_att_p_value, event_study_ses, event_study_cis, event_study_p_values, bootstrap_distribution=None)
Parameters:
Return type:

None