Inference on Counterfactual Distributions

Understand

Counterfactual distributions are important ingredients for policy analysis and decomposition analysis in empirical economics.

• In this article we develop modeling and inference tools for counterfactual distributions based on regression methods.
• The counterfactual scenarios that we consider consist of ceteris paribus changes in either the distribution of covariates related to the outcome of interest or the conditional distribution of the outcome given covariates.
• For either of these scenarios we derive joint functional central limit theorems and bootstrap validity results for regression-based estimators of the status quo and counterfactual outcome distributions.