diff_diff.CSBootstrapResults#

class diff_diff.CSBootstrapResults[source]#

Bases: object

Results from Callaway-Sant’Anna multiplier bootstrap inference.

n_bootstrap#

Number of bootstrap iterations.

Type:: int

weight_type#

Type of bootstrap weights used.

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

group_time_ses#

Bootstrap SEs for each ATT(g,t).

Type:: Dict[Tuple[Any, Any], float]

group_time_cis#

Bootstrap CIs for each ATT(g,t).

Type:: Dict[Tuple[Any, Any], Tuple[float, float]]

group_time_p_values#

Bootstrap p-values for each ATT(g,t).

Type:: Dict[Tuple[Any, Any], float]

event_study_ses#

Bootstrap SEs for event study effects.

Type:: Optional[Dict[int, float]]

event_study_cis#

Bootstrap CIs for event study effects.

Type:: Optional[Dict[int, Tuple[float, float]]]

event_study_p_values#

Bootstrap p-values for event study effects.

Type:: Optional[Dict[int, float]]

group_effect_ses#

Bootstrap SEs for group effects.

Type:: Optional[Dict[Any, float]]

group_effect_cis#

Bootstrap CIs for group effects.

Type:: Optional[Dict[Any, Tuple[float, float]]]

group_effect_p_values#

Bootstrap p-values for group effects.

Type:: Optional[Dict[Any, float]]

bootstrap_distribution#

Full bootstrap distribution of overall ATT (if requested).

Type:: Optional[np.ndarray]

overall_att_es_se#

Bootstrap standard error for the paper Eq. 4.14 overall (event-study average).

Type:: Optional[float]

overall_att_es_ci#

Bootstrap confidence interval for the Eq. 4.14 overall.

Type:: Optional[Tuple[float, float]]

overall_att_es_p_value#

Bootstrap p-value for the Eq. 4.14 overall.

Type:: Optional[float]

Methods

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

Attributes

`bootstrap_distribution`
`cband_crit_value`
`event_study_cis`
`event_study_p_values`
`event_study_ses`
`group_effect_cis`
`group_effect_p_values`
`group_effect_ses`
`overall_att_es_ci`
`overall_att_es_p_value`
`overall_att_es_se`
`n_bootstrap`
`weight_type`
`alpha`
`overall_att_se`
`overall_att_ci`
`overall_att_p_value`
`group_time_ses`
`group_time_cis`
`group_time_p_values`

__init__(n_bootstrap, weight_type, alpha, overall_att_se, overall_att_ci, overall_att_p_value, group_time_ses, group_time_cis, group_time_p_values, event_study_ses=None, event_study_cis=None, event_study_p_values=None, group_effect_ses=None, group_effect_cis=None, group_effect_p_values=None, bootstrap_distribution=None, cband_crit_value=None, overall_att_es_se=None, overall_att_es_ci=None, overall_att_es_p_value=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)
group_time_ses (Dict[Tuple[Any, Any], float])
group_time_cis (Dict[Tuple[Any, Any], Tuple[float, float]])
group_time_p_values (Dict[Tuple[Any, Any], 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_effect_ses (Dict[Any, float] | None)
group_effect_cis (Dict[Any, Tuple[float, float]] | None)
group_effect_p_values (Dict[Any, float] | None)
bootstrap_distribution (ndarray | None)
cband_crit_value (float | None)
overall_att_es_se (float | None)
overall_att_es_ci (Tuple[float, float] | None)
overall_att_es_p_value (float | None)

Return type:

None

classmethod __new__(*args, **kwargs)#