Essays on semi-/non-parametric methods in econometrics

dc.contributor.advisorDonald, Stephen G.
dc.contributor.committeeMemberAbrevaya, Jason
dc.contributor.committeeMemberHan, Sukjin
dc.contributor.committeeMemberShively, Thomas
dc.creatorLee, Sungwon
dc.date.accessioned2018-08-21T14:47:18Z
dc.date.available2018-08-21T14:47:18Z
dc.date.created2018-05
dc.date.issued2018-05-02
dc.date.submittedMay 2018
dc.date.updated2018-08-21T14:47:18Z
dc.description.abstractMy dissertation contains three chapters focusing on semi-/non-parametric models in econometrics. The first chapter, which is a joint work with Sukjin Han, considers parametric/semiparametric estimation and inference in a class of bivariate threshold crossing models with dummy endogenous variables. We investigate the consequences of common practices employed by empirical researchers using this class of models, such as the specification of the joint distribution of the unobservables to be a bivariate normal distribution, resulting in a bivariate probit model. To address the problem of misspecification, we propose a semiparametric estimation framework with parametric copula and nonparametric marginal distributions. This specification is an attempt to ensure robustness while achieving point identification and efficient estimation. We establish asymptotic theory for the sieve maximum likelihood estimators that can be used to conduct inference on the individual structural parameters and the average treatment effects. Numerical studies suggest the sensitivity of parametric specification and the robustness of semiparametric estimation. This paper also shows that the absence of excluded instruments may result in the failure of identification, unlike what some practitioners believe. The second chapter develops nonparametric significance tests for quantile regression models with duration outcomes. It is common for empirical studies to specify models with many covariates to eliminate the omitted variable bias, even if some of them are potentially irrelevant. In the case where models are nonparametrically specified, such a practice results in the curse of dimensionality. I adopt the integrated conditional moment (ICM) approach, which was developed by Bierens (1982) and Bierens (1990) to construct test statistics. The proposed test statistics are functionals of a stochastic process which converges weakly to a centered Gaussian process. The test has non-trivial power against local alternatives at the parametric rate. A subsampling procedure is proposed to obtain critical values. The third chapter considers identification of treatment effect and its distribution under some distributional assumptions. I assume that a binary treatment is endogenously determined. The main identification objects are the quantile treatment effect and the distribution of the treatment effect. I construct a counterfactual model and apply Manski's approach (Manski (1990)) to find the quantile treatment effects. For the distribution of the treatment effect, I adapt the approach proposed by Fan and Park (2010). Some distributional assumptions called stochastic dominance are imposed on the model to tighten the bounds on the parameters of interest. It also provides confidence regions for identified sets that are pointwise consistent in level. An empirical study on the return to college confirms that the stochastic dominance assumptions improve the bounds on the distribution of the treatment effect.
dc.description.departmentEconomics
dc.format.mimetypeapplication/pdf
dc.identifierdoi:10.15781/T2DZ03M0R
dc.identifier.urihttp://hdl.handle.net/2152/68088
dc.language.isoen
dc.subjectSemiparametric
dc.subjectNonparametric
dc.titleEssays on semi-/non-parametric methods in econometrics
dc.typeThesis
dc.type.materialtext
thesis.degree.departmentEconomics
thesis.degree.disciplineEconomics
thesis.degree.grantorThe University of Texas at Austin
thesis.degree.levelDoctoral
thesis.degree.nameDoctor of Philosophy

Access full-text files

Original bundle

Now showing 1 - 1 of 1
Loading...
Thumbnail Image
Name:
LEE-DISSERTATION-2018.pdf
Size:
1.01 MB
Format:
Adobe Portable Document Format

License bundle

Now showing 1 - 2 of 2
No Thumbnail Available
Name:
PROQUEST_LICENSE.txt
Size:
4.45 KB
Format:
Plain Text
Description:
No Thumbnail Available
Name:
LICENSE.txt
Size:
1.84 KB
Format:
Plain Text
Description: