diff_diff.ImputationBootstrapResults

class diff_diff.ImputationBootstrapResults[source]

Bases: object

Results from ImputationDiD bootstrap inference.

Bootstrap is a library extension beyond Borusyak et al. (2024), which proposes only analytical inference via the conservative variance estimator. Provided for consistency with CallawaySantAnna and SunAbraham.

n_bootstrap

Number of bootstrap iterations.

Type:: int

weight_type

Type of bootstrap weights: “rademacher”, “mammen”, or “webb”.

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

overall_att_p_value

Bootstrap p-value for overall ATT.

Type:: float

event_study_ses

Bootstrap SEs for event study effects.

Type:: dict, optional

event_study_cis

Bootstrap CIs for event study effects.

Type:: dict, optional

event_study_p_values

Bootstrap p-values for event study effects.

Type:: dict, optional

group_ses

Bootstrap SEs for group effects.

Type:: dict, optional

group_cis

Bootstrap CIs for group effects.

Type:: dict, optional

group_p_values

Bootstrap p-values for group effects.

Type:: dict, optional

bootstrap_distribution

Full bootstrap distribution of overall ATT.

Type:: np.ndarray, optional

__init__(n_bootstrap, weight_type, alpha, overall_att_se, overall_att_ci, overall_att_p_value, event_study_ses=None, event_study_cis=None, event_study_p_values=None, group_ses=None, group_cis=None, group_p_values=None, bootstrap_distribution=None)

Parameters:

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] | None)
event_study_cis (Dict[int, Tuple[float, float]] | None)
event_study_p_values (Dict[int, float] | None)
group_ses (Dict[Any, float] | None)
group_cis (Dict[Any, Tuple[float, float]] | None)
group_p_values (Dict[Any, float] | None)
bootstrap_distribution (ndarray | None)

Return type:

None

Methods

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

Attributes

`bootstrap_distribution`
`event_study_cis`
`event_study_p_values`
`event_study_ses`
`group_cis`
`group_p_values`
`group_ses`
`n_bootstrap`
`weight_type`
`alpha`
`overall_att_se`
`overall_att_ci`
`overall_att_p_value`

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] | None = None

event_study_cis: Dict[int, Tuple[float, float]] | None = None

event_study_p_values: Dict[int, float] | None = None

group_ses: Dict[Any, float] | None = None

group_cis: Dict[Any, Tuple[float, float]] | None = None

group_p_values: Dict[Any, float] | None = None

bootstrap_distribution: ndarray | None = None

__init__(n_bootstrap, weight_type, alpha, overall_att_se, overall_att_ci, overall_att_p_value, event_study_ses=None, event_study_cis=None, event_study_p_values=None, group_ses=None, group_cis=None, group_p_values=None, bootstrap_distribution=None)

Parameters:

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] | None)
event_study_cis (Dict[int, Tuple[float, float]] | None)
event_study_p_values (Dict[int, float] | None)
group_ses (Dict[Any, float] | None)
group_cis (Dict[Any, Tuple[float, float]] | None)
group_p_values (Dict[Any, float] | None)
bootstrap_distribution (ndarray | None)

Return type:

None