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 observations.

Type:: int

n_control

Number of control 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

__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)

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)

Return type:

None

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`
`coefficients`
`fitted_values`
`interaction_indices`
`is_significant`	Check if the average ATT is statistically significant at the alpha level.
`post_period_effects`	Post-period effects only.
`pre_period_effects`	Pre-period effects only (for parallel trends assessment).
`r_squared`
`reference_period`
`residuals`
`significance_stars`	Return significance stars for the average ATT based on p-value.
`vcov`
`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`

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 = 0.05

coefficients: Dict[str, float] | None = None

vcov: ndarray | None = None

residuals: ndarray | None = None

fitted_values: ndarray | None = None

r_squared: float | None = None

reference_period: Any | None = None

interaction_indices: Dict[Any, int] | None = None

__repr__()[source]

Concise string representation.

Return type:: str

property pre_period_effects: Dict[Any, PeriodEffect]: Pre-period effects only (for parallel trends assessment).

property post_period_effects: Dict[Any, PeriodEffect]: Post-period effects only.

summary(alpha=None)[source]

Generate a formatted summary of the estimation results.

Parameters:: alpha (float, optional) – Significance level for confidence intervals. Defaults to the alpha used during estimation.
Returns:: Formatted summary table.
Return type:: str

print_summary(alpha=None)[source]

Print the summary to stdout.

Parameters:: alpha (float | None)
Return type:: None

get_effect(period)[source]

Get the treatment effect for a specific period.

Parameters:: period (any) – The period identifier.
Returns:: The treatment effect for the specified period.
Return type:: PeriodEffect
Raises:: KeyError – If the period is not found in post-treatment periods.

to_dict()[source]

Convert results to a dictionary.

Returns:: Dictionary containing all estimation results.
Return type:: Dict[str, Any]

to_dataframe()[source]

Convert period-specific effects to a pandas DataFrame.

Returns:: DataFrame with one row per estimated period (pre and post).
Return type:: pd.DataFrame

property is_significant: bool: Check if the average ATT is statistically significant at the alpha level.

property significance_stars: str: Return significance stars for the average ATT based on p-value.

__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)

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)

Return type:

None