A Complete Framework for Model-Free Difference-in-Differences Estimation

Daniel J. Henderson and Stefan Sperlich

Foundations and Trends(R) in Econometrics, 2023, vol. 12, issue 3, 232-323

Abstract: We propose a complete framework for data-driven difference-in-differences analysis with covariates, in particular nonparametric estimation and testing. We start with simultaneously choosing confounders and a scale of the outcome along identification conditions. We estimate first heterogeneous treatment effects stratified along the covariates, then the average effect(s) for the treated. We provide the asymptotic and finite sample behavior of our estimators and tests, bootstrap procedures for their standard errors and p-values, and an automatic bandwidth choice. The pertinence of our methods is shown with a study of the impact of the Deferred Action for Childhood Arrivals program on educational outcomes for non-citizen immigrants in the US.

Date: 2023
References: Add references at CitEc
Citations:

Downloads: (external link)
http://dx.doi.org/10.1561/0800000046 (application/xml)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:now:fnteco:0800000046

Access Statistics for this article

More articles in Foundations and Trends(R) in Econometrics from now publishers
Bibliographic data for series maintained by Lucy Wiseman ().