`statspai.bunching`¶

bunching ¶

Bunching Estimator for kink/notch analysis.

Estimates behavioural responses to policy thresholds (tax kinks, regulatory notches) by comparing the observed distribution of a running variable to a counterfactual polynomial distribution.

References

Kleven, H. J. & Waseem, M. (2013). Using Notches to Uncover Optimization Frictions and Structural Elasticities. QJE, 128(2), 669-723. [@kleven2013using]

Chetty, R., Friedman, J. N., Olsen, T., & Pistaferri, L. (2011). Adjustment Costs, Firm Responses, and Micro vs. Macro Labor Supply Elasticities. QJE, 126(2), 749-804. [@chetty2011adjustment]

BunchingEstimator ¶

Bunching estimator for kink/notch designs.

Parameters:

Name	Type	Default
`data`	`DataFrame`	required
`running_var`	`str`	required
`threshold`	`float`	required
`bin_width`	`float`	`None`
`n_bins`	`int`	`50`
`poly_order`	`int`	`7`
`bunch_region`	`tuple`	`None`
`exclude_region`	`tuple`	`None`
`dt`	`float`	`None`
`design`	`str`	`'kink'`
`n_bootstrap`	`int`	`200`
`alpha`	`float`	`0.05`
`random_state`	`int`	`42`

fit ¶

fit() -> CausalResult

Run bunching estimation.

NotchResult ¶

Result container for notch bunching analysis.

Attributes:

Name	Type	Description
`excess_bunching`	`float`	Excess mass B in the bunching region (below the notch).
`missing_mass`	`float`	Missing mass H above the notch (the "hole").
`marginal_buncher`	`float`	Upper bound of the dominated region (x*).
`elasticity`	`float or None`	Structural elasticity estimate (if notch_size provided).
`se_bunching`	`float`	Bootstrap standard error of excess bunching.
`se_elasticity`	`float or None`	Bootstrap standard error of elasticity (if notch_size provided).
`pvalue`	`float`	P-value for H0: excess_bunching = 0.
`ci`	`tuple of (float, float)`	Confidence interval for excess bunching.
`notch_point`	`float`	Location of the notch.
`bin_centers`	`ndarray`	Bin centres of the histogram.
`observed`	`ndarray`	Observed bin counts.
`counterfactual`	`ndarray`	Counterfactual polynomial counts.
`n_obs`	`int`	Number of observations.
`causal_result`	`CausalResult`	Full CausalResult object for interoperability.

summary ¶

summary() -> str

Return a formatted summary table.

plot ¶

plot(figsize=(10, 6), title=None)

Plot observed histogram with counterfactual overlay.

Returns:

Type	Description
`(fig, ax) : matplotlib figure and axes`

GeneralBunchingResult `dataclass` ¶

Output of high-order bunching design.

KinkUnifiedResult `dataclass` ¶

Joint RDD + RKD + Bunching estimate at a common cutoff.

general_bunching ¶

general_bunching(data: DataFrame, running: str, cutoff: float = 0.0, bandwidth: float = 1.0, bin_width: Optional[float] = None, polynomial_order: int = 4, alpha: float = 0.05, n_boot: int = 200, seed: int = 0) -> GeneralBunchingResult

High-order bunching design with bias correction.