diff_diff.TwoStageDiDResults#

class diff_diff.TwoStageDiDResults[source]#

Bases: object

Results from Gardner (2022) two-stage DiD estimation.

treatment_effects#

Per-observation treatment effects with columns: unit, time, tau_hat, weight. tau_hat is the residualized outcome y_tilde for treated observations; weight is 1/n_treated.

Type:: pd.DataFrame

overall_att#

Overall average treatment effect on the treated.

Type:: float

overall_se#

Standard error of overall ATT (GMM sandwich).

Type:: float

overall_t_stat#

T-statistic for overall ATT.

Type:: float

overall_p_value#

P-value for overall ATT.

Type:: float

overall_conf_int#

Confidence interval for overall ATT.

Type:: tuple

event_study_effects#

Dictionary mapping relative time h to effect dict with keys: ‘effect’, ‘se’, ‘t_stat’, ‘p_value’, ‘conf_int’, ‘n_obs’.

Type:: dict, optional

group_effects#

Dictionary mapping cohort g to effect dict.

Type:: dict, optional

groups#

List of treatment cohorts.

Type:: list

time_periods#

List of all time periods.

Type:: list

n_obs#

Total number of observations.

Type:: int

n_treated_obs#

Number of treated observations.

Type:: int

n_untreated_obs#

Number of untreated observations.

Type:: int

n_treated_units#

Number of ever-treated units.

Type:: int

n_control_units#

Number of units contributing to untreated observations.

Type:: int

alpha#

Significance level used.

Type:: float

bootstrap_results#

Bootstrap inference results.

Type:: TwoStageBootstrapResults, optional

Methods

`__init__`(treatment_effects, overall_att, ...)
`print_summary`([alpha])	Print summary to stdout.
`summary`([alpha])	Generate formatted summary of estimation results.
`to_dataframe`([level])	Convert results to DataFrame.
`to_dict`()	Convert headline results to a dictionary.

Attributes

`alpha`
`anticipation`
`att`
`bootstrap_results`
`cluster_name`
`coef_var`	SE / abs(overall ATT).
`conf_int`
`is_significant`	Check if overall ATT is significant.
`n_clusters`
`p_value`
`se`
`significance_stars`	Significance stars for overall ATT.
`survey_metadata`
`t_stat`
`vcov_type`
`treatment_effects`
`overall_att`
`overall_se`
`overall_t_stat`
`overall_p_value`
`overall_conf_int`
`event_study_effects`
`group_effects`
`groups`
`time_periods`
`n_obs`
`n_treated_obs`
`n_untreated_obs`
`n_treated_units`
`n_control_units`

__init__(treatment_effects, overall_att, overall_se, overall_t_stat, overall_p_value, overall_conf_int, event_study_effects, group_effects, groups, time_periods, n_obs, n_treated_obs, n_untreated_obs, n_treated_units, n_control_units, alpha=0.05, anticipation=0, bootstrap_results=None, survey_metadata=None, vcov_type='hc1', cluster_name=None, n_clusters=None)#

Parameters:

treatment_effects (DataFrame)
overall_att (float)
overall_se (float)
overall_t_stat (float)
overall_p_value (float)
overall_conf_int (Tuple[float, float])
event_study_effects (Dict[int, Dict[str, Any]] | None)
group_effects (Dict[Any, Dict[str, Any]] | None)
groups (List[Any])
time_periods (List[Any])
n_obs (int)
n_treated_obs (int)
n_untreated_obs (int)
n_treated_units (int)
n_control_units (int)
alpha (float)
anticipation (int)
bootstrap_results (TwoStageBootstrapResults | None)
survey_metadata (Any | None)
vcov_type (str)
cluster_name (str | None)
n_clusters (int | None)

Return type:

None

classmethod __new__(*args, **kwargs)#