diff_diff.ContinuousDiDResults#

class diff_diff.ContinuousDiDResults[source]#

Bases: object

Results from Continuous Difference-in-Differences estimation.

Implements Callaway, Goodman-Bacon & Sant’Anna (2024).

dose_response_att#

ATT(d) dose-response curve.

Type:: DoseResponseCurve

dose_response_acrt#

ACRT(d) dose-response curve.

Type:: DoseResponseCurve

overall_att#

Binarized overall ATT (ATT^{loc} under PT, equals ATT^{glob} under SPT).

Type:: float

overall_acrt#

Plug-in overall ACRT^{glob}.

Type:: float

group_time_effects#

Per (g,t) cell results.

Type:: dict

base_period#

Base period strategy ("varying" or "universal").

Type:: str

anticipation#

Number of anticipation periods.

Type:: int

n_bootstrap#

Number of bootstrap iterations used.

Type:: int

bootstrap_weights#

Bootstrap weight type ("rademacher", "mammen", or "webb").

Type:: str

seed#

Random seed used for bootstrap.

Type:: int or None

rank_deficient_action#

How rank deficiency is handled ("warn", "error", "silent").

Type:: str

Methods

`__init__`(dose_response_att, ...[, alpha, ...])
`print_summary`([alpha])	Print summary to stdout.
`summary`([alpha])	Generate formatted summary.
`to_dataframe`([level])	Convert results to DataFrame.

Attributes

`alpha`
`anticipation`
`att`
`base_period`
`bootstrap_weights`
`coef_var`	SE / abs(overall ATT).
`conf_int`
`control_group`
`degree`
`event_study_effects`
`is_significant`	Check if overall ATT is significant.
`n_bootstrap`
`num_knots`
`overall_conf_int`
`overall_p_value`
`overall_se`
`overall_t_stat`
`p_value`
`rank_deficient_action`
`se`
`seed`
`significance_stars`	Significance stars for overall ATT.
`survey_metadata`
`t_stat`
`dose_response_att`
`dose_response_acrt`
`overall_att`
`overall_att_se`
`overall_att_t_stat`
`overall_att_p_value`
`overall_att_conf_int`
`overall_acrt`
`overall_acrt_se`
`overall_acrt_t_stat`
`overall_acrt_p_value`
`overall_acrt_conf_int`
`group_time_effects`
`dose_grid`
`groups`
`time_periods`
`n_obs`
`n_treated_units`
`n_control_units`

__init__(dose_response_att, dose_response_acrt, overall_att, overall_att_se, overall_att_t_stat, overall_att_p_value, overall_att_conf_int, overall_acrt, overall_acrt_se, overall_acrt_t_stat, overall_acrt_p_value, overall_acrt_conf_int, group_time_effects, dose_grid, groups, time_periods, n_obs, n_treated_units, n_control_units, alpha=0.05, control_group='never_treated', degree=3, num_knots=0, base_period='varying', anticipation=0, n_bootstrap=0, bootstrap_weights='rademacher', seed=None, rank_deficient_action='warn', event_study_effects=None, survey_metadata=None)#

Parameters:

dose_response_att (DoseResponseCurve)
dose_response_acrt (DoseResponseCurve)
overall_att (float)
overall_att_se (float)
overall_att_t_stat (float)
overall_att_p_value (float)
overall_att_conf_int (Tuple[float, float])
overall_acrt (float)
overall_acrt_se (float)
overall_acrt_t_stat (float)
overall_acrt_p_value (float)
overall_acrt_conf_int (Tuple[float, float])
group_time_effects (Dict[Tuple[Any, Any], Dict[str, Any]])
dose_grid (ndarray)
groups (List[Any])
time_periods (List[Any])
n_obs (int)
n_treated_units (int)
n_control_units (int)
alpha (float)
control_group (str)
degree (int)
num_knots (int)
base_period (str)
anticipation (int)
n_bootstrap (int)
bootstrap_weights (str)
seed (int | None)
rank_deficient_action (str)
event_study_effects (Dict[int, Dict[str, Any]] | None)
survey_metadata (Any | None)

Return type:

None

classmethod __new__(*args, **kwargs)#