diff_diff.wooldridge_results.WooldridgeDiDResults#

class diff_diff.wooldridge_results.WooldridgeDiDResults[source]#

Bases: object

Results from WooldridgeDiD.fit().

Core output is group_time_effects: a dict keyed by (cohort_g, time_t) with per-cell ATT estimates and inference. Call .aggregate(type, weights=...) to compute any of the four jwdid_estat aggregation types under either the default cell-count weighting (weights="cell", matches Stata jwdid_estat) or the paper W2025 opt-in cohort-share weighting (weights="cohort_share", Eqs. 7.4 / 7.6; restricted to type ∈ {"simple", "event"}). cohort_trend_coefs carries Section 8 / Eq. 8.1 estimated δ_g slopes when the fit was produced under WooldridgeDiD(cohort_trends=True). aggregation_weights is keyed by aggregation type and records the active weighting scheme that wrote to each cached surface (surfaced in summary() / to_dataframe() / __repr__).

Methods

`__init__`(group_time_effects, overall_att, ...)
`aggregate`(type[, weights])	Compute and store one of the four jwdid_estat aggregation types.
`plot_event_study`([weights])	Event study plot.
`summary`([aggregation])	Print formatted summary table.
`to_dataframe`([aggregation])	Export aggregated effects to a DataFrame.

Attributes

`alpha`
`anticipation`
`att`
`calendar_effects`
`cluster_name`
`cohort_trends`
`conf_int`
`conley_lag_cutoff`
`control_group`
`event_study_effects`
`group_effects`
`method`
`n_clusters`
`n_control_units`
`n_obs`
`n_treated_units`
`p_value`
`se`
`survey_metadata`
`t_stat`
`vcov_type`
`group_time_effects`	key=(g,t), value={att, se, t_stat, p_value, conf_int}
`overall_att`
`overall_se`
`overall_t_stat`
`overall_p_value`
`overall_conf_int`
`groups`
`time_periods`
`cohort_trend_coefs`
`aggregation_weights`

__init__(group_time_effects, overall_att, overall_se, overall_t_stat, overall_p_value, overall_conf_int, group_effects=None, calendar_effects=None, event_study_effects=None, method='ols', control_group='not_yet_treated', groups=<factory>, time_periods=<factory>, n_obs=0, n_treated_units=0, n_control_units=0, alpha=0.05, anticipation=0, survey_metadata=None, vcov_type='hc1', cluster_name=None, n_clusters=None, conley_lag_cutoff=None, cohort_trend_coefs=<factory>, _bootstrap_used=False, cohort_trends=False, aggregation_weights=<factory>, _gt_weights=<factory>, _n_g_per_cohort=<factory>, _gt_vcov=None, _gt_keys=<factory>, _df_survey=None, _bm_per_cell_dof=<factory>, _bm_artifacts=None, _df_one_way=None)#

Parameters:

group_time_effects (Dict[Tuple[Any, Any], Dict[str, Any]])
overall_att (float)
overall_se (float)
overall_t_stat (float)
overall_p_value (float)
overall_conf_int (Tuple[float, float])
group_effects (Dict[Any, Dict] | None)
calendar_effects (Dict[Any, Dict] | None)
event_study_effects (Dict[int, Dict] | None)
method (str)
control_group (str)
groups (List[Any])
time_periods (List[Any])
n_obs (int)
n_treated_units (int)
n_control_units (int)
alpha (float)
anticipation (int)
survey_metadata (Any | None)
vcov_type (str)
cluster_name (str | None)
n_clusters (int | None)
conley_lag_cutoff (int | None)
cohort_trend_coefs (Dict[Any, float])
_bootstrap_used (bool)
cohort_trends (bool)
aggregation_weights (Dict[str, str])
_gt_weights (Dict[Tuple[Any, Any], int])
_n_g_per_cohort (Dict[Any, int])
_gt_vcov (ndarray | None)
_gt_keys (List[Tuple[Any, Any]])
_df_survey (int | None)
_bm_per_cell_dof (Dict[Tuple[Any, Any], float])
_bm_artifacts (Tuple[ndarray, ndarray, ndarray, Dict[Tuple[Any, Any], int]] | None)
_df_one_way (float | None)

Return type:

None

classmethod __new__(*args, **kwargs)#