Sharp bounds on the distribution of treatment effects and their statistical inference

Yanqin Fan, Sang-Soo Park

Research output: Contribution to journalArticle

37 Citations (Scopus)

Abstract

In this paper, we propose nonparametric estimators of sharp bounds on the distribution of treatment effects of a binary treatment and establish their asymptotic distributions. We note the possible failure of the standard bootstrap with the same sample size and apply the fewer-than-n bootstrap to making inferences on these bounds. The finite sample performances of the confidence intervals for the bounds based on normal critical values, the standard bootstrap, and the fewer-than-n bootstrap are investigated via a simulation study. Finally we establish sharp bounds on the treatment effect distribution when covariates are available.

Original languageEnglish
Pages (from-to)931-951
Number of pages21
JournalEconometric Theory
Volume26
Issue number3
DOIs
Publication statusPublished - 2010 Jun 1
Externally publishedYes

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confidence
simulation
performance
Bootstrap
Statistical inference
Treatment effects
Finite sample
Critical value
Covariates
Asymptotic distribution
Simulation study
Inference
Estimator
Sample size
Confidence interval

ASJC Scopus subject areas

  • Economics and Econometrics
  • Social Sciences (miscellaneous)

Cite this

Sharp bounds on the distribution of treatment effects and their statistical inference. / Fan, Yanqin; Park, Sang-Soo.

In: Econometric Theory, Vol. 26, No. 3, 01.06.2010, p. 931-951.

Research output: Contribution to journalArticle

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