diff_diff.PreTrendsPowerResults

class diff_diff.PreTrendsPowerResults[source]

Bases: object

Results from pre-trends power analysis.

power

Power to detect the specified violation pattern at given alpha.

Type:: float

mdv

Minimum detectable violation (smallest M detectable at target power).

Type:: float

violation_magnitude

The magnitude of violation tested (M parameter).

Type:: float

violation_type

Type of violation pattern (‘linear’, ‘constant’, ‘last_period’, ‘custom’).

Type:: str

alpha

Significance level for the pre-trends test.

Type:: float

target_power

Target power level used for MDV calculation.

Type:: float

n_pre_periods

Number of pre-treatment periods in the event study.

Type:: int

test_statistic

Expected test statistic under the specified violation.

Type:: float

critical_value

Critical value for the pre-trends test.

Type:: float

noncentrality

Non-centrality parameter under the alternative hypothesis.

Type:: float

pre_period_effects

Estimated pre-period effects from the event study.

Type:: np.ndarray

pre_period_ses

Standard errors of pre-period effects.

Type:: np.ndarray

vcov

Variance-covariance matrix of pre-period effects.

Type:: np.ndarray

__init__(power, mdv, violation_magnitude, violation_type, alpha, target_power, n_pre_periods, test_statistic, critical_value, noncentrality, pre_period_effects, pre_period_ses, vcov, original_results=None)

Parameters:

power (float)
mdv (float)
violation_magnitude (float)
violation_type (str)
alpha (float)
target_power (float)
n_pre_periods (int)
test_statistic (float)
critical_value (float)
noncentrality (float)
pre_period_effects (ndarray)
pre_period_ses (ndarray)
vcov (ndarray)
original_results (Any | None)

Return type:

None

Methods

`__init__`(power, mdv, violation_magnitude, ...)
`power_at`(M)	Compute power to detect a specific violation magnitude.
`print_summary`()	Print summary to stdout.
`summary`()	Generate formatted summary of pre-trends power analysis.
`to_dataframe`()	Convert results to DataFrame.
`to_dict`()	Convert results to dictionary.

Attributes

`is_informative`	Check if the pre-trends test is informative.
`original_results`
`power_adequate`	Check if power meets the target threshold.
`power`
`mdv`
`violation_magnitude`
`violation_type`
`alpha`
`target_power`
`n_pre_periods`
`test_statistic`
`critical_value`
`noncentrality`
`pre_period_effects`
`pre_period_ses`
`vcov`

power: float

mdv: float

violation_magnitude: float

violation_type: str

alpha: float

target_power: float

n_pre_periods: int

test_statistic: float

critical_value: float

noncentrality: float

pre_period_effects: ndarray

pre_period_ses: ndarray

vcov: ndarray

original_results: Any | None = None

property is_informative: bool

Check if the pre-trends test is informative.

A pre-trends test is considered informative if the MDV is reasonably small relative to typical effect sizes. This is a heuristic check; see the summary for interpretation guidance.

property power_adequate: bool: Check if power meets the target threshold.

summary()[source]

Generate formatted summary of pre-trends power analysis.

Returns:: Formatted summary.
Return type:: str

print_summary()[source]

Print summary to stdout.

Return type:: None

to_dict()[source]

Convert results to dictionary.

Return type:: Dict[str, Any]

to_dataframe()[source]

Convert results to DataFrame.

Return type:: DataFrame

power_at(M)[source]

Compute power to detect a specific violation magnitude.

This method allows computing power at different M values without re-fitting the model, using the stored variance-covariance matrix.

Parameters:: M (float) – Violation magnitude to evaluate.
Returns:: Power to detect violation of magnitude M.
Return type:: float

__init__(power, mdv, violation_magnitude, violation_type, alpha, target_power, n_pre_periods, test_statistic, critical_value, noncentrality, pre_period_effects, pre_period_ses, vcov, original_results=None)

Parameters:

power (float)
mdv (float)
violation_magnitude (float)
violation_type (str)
alpha (float)
target_power (float)
n_pre_periods (int)
test_statistic (float)
critical_value (float)
noncentrality (float)
pre_period_effects (ndarray)
pre_period_ses (ndarray)
vcov (ndarray)
original_results (Any | None)

Return type:

None