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 (Wald only; NaN for NIS fits).

Type:: float

critical_value#

Critical value for the pre-trends test.

Type:: float

noncentrality#

Non-centrality parameter under the alternative hypothesis (Wald only; NaN for NIS fits).

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

pretest_form#

Pretest acceptance-region form used: 'nis' (no-individually- significant box probability — Roth 2022 Section II.A-B, default for new fits) or 'wald' (noncentral-chi-squared on the quadratic form delta' Sigma_22^{-1} delta — paper-supported alternative, retained for backwards compatibility with shipped numerical baselines).

Type:: str

nis_box_probability#

Acceptance probability P(beta_hat_pre in B_NIS(Sigma)) under the alternative M * weights. NIS-only; NaN for Wald fits.

Type:: float

violation_weights#

The violation-direction vector used at fit time. Populated for all violation types on fresh fits. Normalization depends on the type so that M always matches the documented per-pattern contract:

linear threaded with relative_times (post PR-B Step 4): |t| directly, NOT L2-normalized, so δ_t = M·|t| and the reported MDV equals Roth’s γ exactly.
linear without relative_times (legacy): [n_pre-1, ..., 0] L2-normalized.
constant (post PR-B R13): [1, ..., 1] directly, NOT L2-normalized, so δ_t = M is a true per-period level shift.
last_period: [0, ..., 0, 1] (already unit-norm).
custom: user vector L2-normalized to unit norm.

Old serialized results may have None here; power_at() falls back to reconstruction in that case (with the PR-A NotImplementedError guard retained only for violation_type='custom' with violation_weights=None).

Type:: np.ndarray, optional

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 JSON-serializable dictionary.

Attributes

`covariance_source`
`is_informative`	Check if the pre-trends test is informative.
`max_abs_pre_violation`	Largest level-scale pre-period deviation under the MDV.
`nis_box_probability`
`original_results`
`power_adequate`	Check if power meets the target threshold.
`pretest_form`
`violation_weights`
`power`
`mdv`
`violation_magnitude`
`violation_type`
`alpha`
`target_power`
`n_pre_periods`
`test_statistic`
`critical_value`
`noncentrality`
`pre_period_effects`
`pre_period_ses`
`vcov`

__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, pretest_form='wald', nis_box_probability=nan, violation_weights=None, covariance_source='unknown')#

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)
pretest_form (Literal['nis', 'wald'])
nis_box_probability (float)
violation_weights (ndarray | None)
covariance_source (str)

Return type:

None

classmethod __new__(*args, **kwargs)#