Abstract
dc:descriptionNonparametric estimators for average and quantile treatment effects are constructed using fractile graphical analysis under the identifying assumption that selection to treatment is based on observable characteristics. The proposed method has two-steps: first, the propensity score is estimated, and second, a blocking estimation procedure using this estimate is used to compute the treatment effects. In both cases, the estimators are proved to be consistent. Monte Carlo results show a better performance than other procedures based on the propensity score. Finally, these estimators are applied to a job training dataset.
Degree
thesis:*- Name thesis:degree_name
- Ph.D.
- Level thesis:degree_level
- Dissertation
- Discipline thesis:degree_discipline
- Economics
- Grantor
- University of Illinois at Urbana-Champaign
- Year dc:date
- 2015
Author and committee
dc:creator, dc:contributor.*- Author dc:creator
-
- Montes Roja, Gabriel V.
- Contributors dc:contributor
-
- Bera, Anil K.
Subjects
dc:subject × 1Rights
- Language dc:language
- eng
Identifiers
dc:identifier.*- Identifier
- (MiAaPQ)AAI3290324
- OAI identifier oai:identifier
- oai:www.ideals.illinois.edu:2142/85585