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Three Essays on Econometrics.

Kim, Wooyoung.

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Three Essays on Econometrics.

Record Type:	Electronic resources : Monograph/item
Title/Author:	Three Essays on Econometrics./
Author:	Kim, Wooyoung.
Published:	Ann Arbor : ProQuest Dissertations & Theses, : 2019,
Description:	107 p.
Notes:	Source: Dissertations Abstracts International, Volume: 80-09, Section: A.
Contained By:	Dissertations Abstracts International80-09A.
Subject:	Economics. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=13809399
ISBN:	9781392004197

Three Essays on Econometrics.
Kim, Wooyoung.

Three Essays on Econometrics. - Ann Arbor : ProQuest Dissertations & Theses, 2019 - 107 p.

Source: Dissertations Abstracts International, Volume: 80-09, Section: A.

Thesis (Ph.D.)--The University of Wisconsin - Madison, 2019.

This item must not be added to any third party search indexes.

In the first chapter, I propose an averaging estimator with a data-dependent weight for models with a potentially misspecified over-identifying moment inequality condition. I derive the uniform dominance result of the estimator with the infeasible optimal weight to minimize the mean-squared error and propose a plug-in estimator to implement it. Although the plug-in estimator is not consistent because of the inconsistency in the estimation of the slackness parameter, I show that this estimator performs well in simulations in terms of the mean-squared error. In the second chapter, I propose a bootstrap-based confidence interval of a projection of a potentially partially identified parameter which is asymptotically uniformly valid and alleviates projection conservatism. I also suggest the algorithm to implement my approach using the response surface method. The implementation is not costly in terms of computational time. I provide a simulation result of the two-player entry game. Lastly, I illustrate the application of the frequentist's approach to the structural VAR with sign restrictions. In the last chapter, I propose an estimator and an inference method for the low-dimensional parameters of interest in models with high-dimensional controls. The estimator uses principal components regression (PCR) to estimate relevant components of the high-dimensional controls. I adopt the Neyman orthogonalized moment conditions to obtain √N-consistency of my estimator. I derive asymptotic normality of the estimator and develop a consistent estimator for the asymptotic variance. I extend these results to allow for endogeneity of the variables of interest when an instrumental variable is available. In simulations, I compare the mean-squared error and the coverage rate of corresponding confidence intervals of my estimator with several competing estimators for a parameter of interest in different setups. PCR results show correct coverage rate and the smallest mean-squared error when the underlying data generating processes are high-dimensional factor models. I apply my estimator and other parametric alternatives to the estimation and inference of the price coefficient in logit demand models for the U.S. cereal market. Using an instrumental variable does change the estimate of the price coefficient significantly, which implies that researchers should consider potential endogeneity problems even when using high-dimensional controls.

ISBN: 9781392004197Subjects--Topical Terms:

517137
Economics.

Three Essays on Econometrics.
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300 $a 107 p.
500 $a Source: Dissertations Abstracts International, Volume: 80-09, Section: A.
500 $a Publisher info.: Dissertation/Thesis.
500 $a Shi, Xiaoxia;Hansen, Bruce E.
502 $a Thesis (Ph.D.)--The University of Wisconsin - Madison, 2019.
506 $a This item must not be added to any third party search indexes.
506 $a This item must not be sold to any third party vendors.
520 $a In the first chapter, I propose an averaging estimator with a data-dependent weight for models with a potentially misspecified over-identifying moment inequality condition. I derive the uniform dominance result of the estimator with the infeasible optimal weight to minimize the mean-squared error and propose a plug-in estimator to implement it. Although the plug-in estimator is not consistent because of the inconsistency in the estimation of the slackness parameter, I show that this estimator performs well in simulations in terms of the mean-squared error. In the second chapter, I propose a bootstrap-based confidence interval of a projection of a potentially partially identified parameter which is asymptotically uniformly valid and alleviates projection conservatism. I also suggest the algorithm to implement my approach using the response surface method. The implementation is not costly in terms of computational time. I provide a simulation result of the two-player entry game. Lastly, I illustrate the application of the frequentist's approach to the structural VAR with sign restrictions. In the last chapter, I propose an estimator and an inference method for the low-dimensional parameters of interest in models with high-dimensional controls. The estimator uses principal components regression (PCR) to estimate relevant components of the high-dimensional controls. I adopt the Neyman orthogonalized moment conditions to obtain √N-consistency of my estimator. I derive asymptotic normality of the estimator and develop a consistent estimator for the asymptotic variance. I extend these results to allow for endogeneity of the variables of interest when an instrumental variable is available. In simulations, I compare the mean-squared error and the coverage rate of corresponding confidence intervals of my estimator with several competing estimators for a parameter of interest in different setups. PCR results show correct coverage rate and the smallest mean-squared error when the underlying data generating processes are high-dimensional factor models. I apply my estimator and other parametric alternatives to the estimation and inference of the price coefficient in logit demand models for the U.S. cereal market. Using an instrumental variable does change the estimate of the price coefficient significantly, which implies that researchers should consider potential endogeneity problems even when using high-dimensional controls.
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