qte: Quantile Treatment Effects

Provides several methods for computing the Quantile Treatment Effect (QTE) and Quantile Treatment Effect on the Treated (QTT). The main cases covered are (i) Treatment is randomly assigned, (ii) Treatment is as good as randomly assigned after conditioning on some covariates (also called conditional independence or selection on observables) using the methods developed in Firpo (2007) <doi:10.1111/j.1468-0262.2007.00738.x>, (iii) Identification is based on a Difference in Differences assumption (several varieties are available in the package e.g. Athey and Imbens (2006) <doi:10.1111/j.1468-0262.2006.00668.x> Callaway and Li (2019) <https://ssrn.com/abstract=3013341>, Callaway, Li, and Oka (2018) <doi:10.1016/j.jeconom.2018.06.008>).

Version: 1.3.0
Depends: R (≥ 2.10)
Imports: Hmisc, parallel, quantreg, BMisc, formula.tools, ggplot2, texreg, pbapply, knitr, msm
Suggests: rmarkdown
Published: 2019-06-10
Author: Brantly Callaway [aut, cre]
Maintainer: Brantly Callaway <brantly.callaway at temple.edu>
License: GPL-2
NeedsCompilation: no
Materials: README NEWS
CRAN checks: qte results


Reference manual: qte.pdf
Vignettes: ddid2
Quantile Treatment Effects in R


Package source: qte_1.3.0.tar.gz
Windows binaries: r-devel: qte_1.3.0.zip, r-release: qte_1.3.0.zip, r-oldrel: qte_1.3.0.zip
macOS binaries: r-release (arm64): qte_1.3.0.tgz, r-oldrel (arm64): qte_1.3.0.tgz, r-release (x86_64): qte_1.3.0.tgz, r-oldrel (x86_64): qte_1.3.0.tgz
Old sources: qte archive

Reverse dependencies:

Reverse imports: csabounds


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