Eefficient estimation of the dose-response function under ignorability using subclassification on the covariates

Matias D. Cattaneo, Max H. Farrell

Research output: Chapter in Book/Report/Conference proceedingChapter

12 Scopus citations

Abstract

This chapter studies the large sample properties of a subclassificationbased estimator of the dose-response function under ignorability. Employing standard regularity conditions, it is shown that the estimator is root-n consistent, asymptotically linear, and semiparametric efficient in large samples. A consistent estimator of the standard-error is also developed under the same assumptions. In a Monte Carlo experiment, we investigate the finite sample performance of this simple and intuitive estimator and compare it to others commonly employed in the literature.

Original languageEnglish (US)
Title of host publicationMissing Data Methods
Subtitle of host publicationCross-Sectional Methods and Applications
EditorsWilliam Greene, David Drukker
Pages93-127
Number of pages35
DOIs
StatePublished - Dec 1 2011

Publication series

NameAdvances in Econometrics
Volume27 A
ISSN (Print)0731-9053

All Science Journal Classification (ASJC) codes

  • Economics and Econometrics

Keywords

  • Blocking
  • Missing data
  • Semiparametric efficiency
  • Stratification
  • Subclassification
  • Treatment effects

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    Cattaneo, M. D., & Farrell, M. H. (2011). Eefficient estimation of the dose-response function under ignorability using subclassification on the covariates. In W. Greene, & D. Drukker (Eds.), Missing Data Methods: Cross-Sectional Methods and Applications (pp. 93-127). [17004565] (Advances in Econometrics; Vol. 27 A). https://doi.org/10.1108/S0731-9053(2011)000027A007