moderndid.ordid#

moderndid.ordid(data, yname, tname, idname=None, treatname=None, xformla=None, panel=True, weightsname=None, boot=False, boot_type='weighted', n_boot=999, inf_func=False)[source]#

Wrap the outcome regression DiD estimators for the ATT.

This function is a wrapper for outcome regression DiD estimators. It calls the appropriate estimator based on the panel argument and performs pre-processing for the data.

Parameters:
datapandas.DataFrame | polars.DataFrame

The input data containing outcome, time, unit ID, treatment, and optionally covariates and weights. Accepts both pandas and polars DataFrames.

ynamestr

Name of the column containing the outcome variable.

tnamestr

Name of the column containing the time periods (must have exactly 2 periods).

idnamestr | None, default None

Name of the column containing the unit ID. Required if panel=True.

treatnamestr

Name of the column containing the treatment group indicator. For panel data: time-invariant indicator (1 if ever treated, 0 if never treated). For repeated cross-sections: treatment status in the post-period.

xformlastr | None, default None

A formula for the covariates to include in the model. Should be of the form “~ X1 + X2” (intercept is always included). If None, equivalent to “~ 1” (intercept only).

panelbool, default True

Whether the data is panel (True) or repeated cross-sections (False). Panel data should be in long format with each row representing a unit-time observation.

weightsnamestr | None, default None

Name of the column containing sampling weights. If None, all observations have equal weight. Weights are normalized to have mean 1.

bootbool, default False

Whether to compute bootstrap standard errors.

boot_type{“weighted”, “multiplier”}, default “weighted”

Type of bootstrap to perform (only relevant if boot=True).

n_bootint, default 999

Number of bootstrap repetitions (only relevant if boot=True).

inf_funcbool, default False

Whether to return the influence function values.

Returns:
ORDIDResult

NamedTuple containing:

  • att: The OR DiD point estimate.

  • se: The OR DiD standard error.

  • uci: The upper bound of a 95% confidence interval.

  • lci: The lower bound of a 95% confidence interval.

  • boots: Bootstrap draws of the ATT if boot=True.

  • att_inf_func: Influence function values if inf_func=True.

  • call_params: Original function call parameters.

  • args: Arguments used in the estimation.

See also

drdid

Doubly robust DiD estimator.

ipwdid

Inverse propensity weighted DiD estimator.

Notes

The outcome regression DiD estimator is based on a linear regression model for the outcome conditional on covariates, time period, and treatment status. For panel data, it estimates the ATT by comparing the change in outcomes for treated units to the predicted change for control units based on their covariate values.

This estimator assumes that the conditional expectation function is correctly specified and that the parallel trends assumption holds conditional on covariates. Unlike the doubly robust estimator, it is not robust to misspecification of the outcome regression model.

References

[1]

Heckman, J., Ichimura, H., and Todd, P. (1997), “Matching as an Econometric Evaluation Estimator: Evidence from Evaluating a Job Training Programme”, Review of Economic Studies, vol. 64(4), p. 605–654. https://doi.org/10.2307/2971733

[2]

Sant’Anna, P. H. C. and Zhao, J. (2020), “Doubly Robust Difference-in-Differences Estimators.” Journal of Econometrics, Vol. 219 (1), pp. 101-122. https://doi.org/10.1016/j.jeconom.2020.06.003

Examples

Estimate the average treatment effect on the treated (ATT) using outcome regression with panel data from a job training program. The outcome regression approach models the conditional expectation of the outcome given covariates.

In [1]: import moderndid
   ...: from moderndid import load_nsw
   ...: 
   ...: nsw_data = load_nsw()
   ...: 
   ...: att_result = moderndid.ordid(
   ...:     data=nsw_data,
   ...:     yname="re",
   ...:     tname="year",
   ...:     idname="id",
   ...:     treatname="experimental",
   ...:     xformla="~ age + educ + black + married + nodegree + hisp + re74",
   ...:     panel=True,
   ...: )
   ...: 

In [2]: print(att_result)
==============================================================================
 Outcome Regression DiD Estimator
==============================================================================
 Computed from 32834 observations and 12 covariates.

┌────────────┬────────────┬──────────┬───────────────────────────┐
│        ATT │ Std. Error │ Pr(>|t|) │ [95% Conf. Interval]      │
├────────────┼────────────┼──────────┼───────────────────────────┤
│ -1300.6446 │   349.8259 │   <0.001 │ [-1986.3033, -614.9859] * │
└────────────┴────────────┴──────────┴───────────────────────────┘

------------------------------------------------------------------------------
 Data Info
------------------------------------------------------------------------------
 Data structure: Panel data

------------------------------------------------------------------------------
 Estimation Details
------------------------------------------------------------------------------
 Estimation method: Ordinary least squares

------------------------------------------------------------------------------
 Inference
------------------------------------------------------------------------------
 Standard errors: Analytical
==============================================================================
 Reference: Heckman et al. (1997), Review of Economic Studies
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