diff_diff.MultiPeriodDiDResults#

class diff_diff.MultiPeriodDiDResults[source]#

Bases: object

Results from a Multi-Period Difference-in-Differences estimation.

Provides access to period-specific treatment effects as well as an aggregate average treatment effect.

period_effects#

Dictionary mapping period identifiers to their PeriodEffect objects. Contains all estimated period effects (pre and post, excluding the reference period which is normalized to zero).

Type:: dict[any, PeriodEffect]

avg_att#

Average Treatment effect on the Treated across post-periods only.

Type:: float

avg_se#

Standard error of the average ATT.

Type:: float

avg_t_stat#

T-statistic for the average ATT.

Type:: float

avg_p_value#

P-value for the null hypothesis that average ATT = 0.

Type:: float

avg_conf_int#

Confidence interval for the average ATT.

Type:: tuple[float, float]

n_obs#

Number of observations used in estimation.

Type:: int

n_treated#

Number of treated units/observations.

Type:: int

n_control#

Number of control units/observations.

Type:: int

pre_periods#

List of pre-treatment period identifiers.

Type:: list

post_periods#

List of post-treatment period identifiers.

Type:: list

reference_period#

The reference (omitted) period. Its coefficient is zero by construction and it is excluded from period_effects.

Type:: any, optional

interaction_indices#

Mapping from period identifier to column index in the full variance-covariance matrix. Used internally for sub-VCV extraction (e.g., by HonestDiD and PreTrendsPower).

Type:: dict, optional

Methods

`__init__`(period_effects, avg_att, avg_se, ...)
`get_effect`(period)	Get the treatment effect for a specific period.
`print_summary`([alpha])	Print the summary to stdout.
`summary`([alpha])	Generate a formatted summary of the estimation results.
`to_dataframe`()	Convert period-specific effects to a pandas DataFrame.
`to_dict`()	Convert results to a dictionary.

Attributes

`alpha`
`att`
`cluster_name`
`coef_var`	SE / abs(overall ATT).
`coefficients`
`conf_int`
`conley_lag_cutoff`
`fitted_values`
`inference_method`
`interaction_indices`
`is_significant`	Check if the average ATT is statistically significant at the alpha level.
`n_bootstrap`
`n_clusters`
`p_value`
`post_period_effects`	Post-period effects only.
`pre_period_effects`	Pre-period effects only (for parallel trends assessment).
`r_squared`
`reference_period`
`residuals`
`se`
`significance_stars`	Return significance stars for the average ATT based on p-value.
`survey_metadata`
`t_stat`
`vcov`
`vcov_type`
`period_effects`
`avg_att`
`avg_se`
`avg_t_stat`
`avg_p_value`
`avg_conf_int`
`n_obs`
`n_treated`
`n_control`
`pre_periods`
`post_periods`

__init__(period_effects, avg_att, avg_se, avg_t_stat, avg_p_value, avg_conf_int, n_obs, n_treated, n_control, pre_periods, post_periods, alpha=0.05, coefficients=None, vcov=None, residuals=None, fitted_values=None, r_squared=None, reference_period=None, interaction_indices=None, survey_metadata=None, inference_method='analytical', n_bootstrap=None, n_clusters=None, vcov_type=None, cluster_name=None, conley_lag_cutoff=None)#

Parameters:

period_effects (Dict[Any, PeriodEffect])
avg_att (float)
avg_se (float)
avg_t_stat (float)
avg_p_value (float)
avg_conf_int (Tuple[float, float])
n_obs (int)
n_treated (int)
n_control (int)
pre_periods (List[Any])
post_periods (List[Any])
alpha (float)
coefficients (Dict[str, float] | None)
vcov (ndarray | None)
residuals (ndarray | None)
fitted_values (ndarray | None)
r_squared (float | None)
reference_period (Any | None)
interaction_indices (Dict[Any, int] | None)
survey_metadata (Any | None)
inference_method (str)
n_bootstrap (int | None)
n_clusters (int | None)
vcov_type (str | None)
cluster_name (str | None)
conley_lag_cutoff (int | None)

Return type:

None

classmethod __new__(*args, **kwargs)#