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Title: Essays on Nonparametric and Semiparametric Identification and Estimation
Resulting in 1 citation.
1. Yang, Shenshen
Essays on Nonparametric and Semiparametric Identification and Estimation
Ph.D. Dissertation, Department of Economics, University of Texas at Austin, 2021
Cohort(s): NLSY79
Publisher: University of Texas at Austin
Keyword(s): College Degree; Educational Returns; Modeling; Statistical Analysis

Permission to reprint the abstract has not been received from the publisher.

This dissertation consists of three chapters in econometric theory, with a focus on identification and estimation of treatment effect in semi-parametric and nonparametric models, when there exists endogeneity problem. These methods are applied on policy and program evaluation in health and labor economics.

In the third chapter, I investigate the partial identification bound for treatment effect in a dynamic setting. First, I develop the sharp partial identification bounds of dynamic treatment effect on conditional transition probabilities when the treatment is randomly assigned. Then I relax the randomization assumption and gives partial identification bounds, under a conditional mean independence assumption. Using MTR and MTS assumptions, this bound is further tightened. These bounds are used on estimating labor market return of college degree in a long term, with data from NLSY79.

Bibliography Citation
Yang, Shenshen. Essays on Nonparametric and Semiparametric Identification and Estimation. Ph.D. Dissertation, Department of Economics, University of Texas at Austin, 2021.