Local regression distribution estimators

Matias D. Cattaneo, Michael Jansson, Xinwei Ma

Research output: Contribution to journalArticlepeer-review

8 Scopus citations


This paper investigates the large sample properties of local regression distribution estimators, which include a class of boundary adaptive density estimators as a prime example. First, we establish a pointwise Gaussian large sample distributional approximation in a unified way, allowing for both boundary and interior evaluation points simultaneously. Using this result, we study the asymptotic efficiency of the estimators, and show that a carefully crafted minimum distance implementation based on “redundant” regressors can lead to efficiency gains. Second, we establish uniform linearizations and strong approximations for the estimators, and employ these results to construct valid confidence bands. Third, we develop extensions to weighted distributions with estimated weights and to local L2 estimation. Finally, we illustrate our methods with two applications in program evaluation: counterfactual density testing, and IV specification and heterogeneity density analysis. Companion software packages in Stata and R are available.

Original languageEnglish (US)
Article number105074
JournalJournal of Econometrics
Issue number2
StatePublished - Mar 2024

All Science Journal Classification (ASJC) codes

  • Applied Mathematics
  • Economics and Econometrics


  • Distribution and density estimation
  • Efficiency
  • Local polynomial methods
  • Optimal kernel
  • Program evaluation
  • Uniform approximation


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