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Essays in Bayesian Econometrics
Essays in Bayesian Econometrics
상세정보
- 자료유형
- 학위논문 서양
- 최종처리일시
- 20260202103509
- ISBN
- 9798280717442
- DDC
- 310
- 서명/저자
- Essays in Bayesian Econometrics
- 발행사항
- [Sl] : Harvard University, 2025
- 발행사항
- Ann Arbor : ProQuest Dissertations & Theses, 2025
- 형태사항
- 315 p
- 주기사항
- Source: Dissertations Abstracts International, Volume: 86-12, Section: B.
- 주기사항
- Advisor: Tamer, Elie.
- 학위논문주기
- Thesis (Ph.D.)--Harvard University, 2025.
- 초록/해제
- 요약This thesis comprises three chapters in Bayesian econometrics. Although the chapters are independent, the unifying theme is that Bayesian inference is semiparametric. That is, the econometric model is either 1. indexed by finite- and infinite-dimensional parameters, or 2. interest is a finite-dimensional transformation of an infinite-dimensional parameter. All chapters are solo-authored.Chapter 1 proposes Bayesian inference for conditional moment equality models. The framework starts with a prior for a conditional distribution and reports a marginal posterior for an estimand that minimizes the distance of the conditional moments to zero. The key theoretical result is a Bernstein-von Mises theorem, establishing asymptotic normality of the minimum distance posterior. Chapter 2 presents a new approach to Bernstein-von Mises theory for partially linear regression models. The idea is to embed an adaptive parametrization of the regression function within a (quasi-)likelihood model, enabling verification of the Bernstein-von Mises theorem using ordinary expansions of the log-likelihood function. The new parametrization alleviates some smoothness restrictions that are encountered in the original parametrization of the model.Chapter 3 introduces a Bayesian inference framework for a linear index threshold-crossing binary choice model subject to a median independence restriction. The proposal exploits an observational equivalence between the model and a probit model with nonparametric heteroskedasticity. This leads to a computationally attractive Bayesian inference procedure in which a Gaussian process forms a conditionally conjugate prior for the natural logarithm of the skedastic function.
- 일반주제명
- Statistics
- 기타저자
- Harvard University Economics
- 기본자료저록
- Dissertations Abstracts International. 86-12B.
- 전자적 위치 및 접속
- 로그인 후 원문을 볼 수 있습니다.
MARC
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■006m o d
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■020 ▼a9798280717442
■035 ▼a(MiAaPQ)AAI32003106
■040 ▼aMiAaPQ▼cMiAaPQ
■0820 ▼a310
■1001 ▼aWalker, Christopher Douglas.▼0(orcid)0009-0005-5056-0019
■24510▼aEssays in Bayesian Econometrics
■260 ▼a[Sl]▼bHarvard University▼c2025
■260 1▼aAnn Arbor▼bProQuest Dissertations & Theses▼c2025
■300 ▼a315 p
■500 ▼aSource: Dissertations Abstracts International, Volume: 86-12, Section: B.
■500 ▼aAdvisor: Tamer, Elie.
■5021 ▼aThesis (Ph.D.)--Harvard University, 2025.
■520 ▼aThis thesis comprises three chapters in Bayesian econometrics. Although the chapters are independent, the unifying theme is that Bayesian inference is semiparametric. That is, the econometric model is either 1. indexed by finite- and infinite-dimensional parameters, or 2. interest is a finite-dimensional transformation of an infinite-dimensional parameter. All chapters are solo-authored.Chapter 1 proposes Bayesian inference for conditional moment equality models. The framework starts with a prior for a conditional distribution and reports a marginal posterior for an estimand that minimizes the distance of the conditional moments to zero. The key theoretical result is a Bernstein-von Mises theorem, establishing asymptotic normality of the minimum distance posterior. Chapter 2 presents a new approach to Bernstein-von Mises theory for partially linear regression models. The idea is to embed an adaptive parametrization of the regression function within a (quasi-)likelihood model, enabling verification of the Bernstein-von Mises theorem using ordinary expansions of the log-likelihood function. The new parametrization alleviates some smoothness restrictions that are encountered in the original parametrization of the model.Chapter 3 introduces a Bayesian inference framework for a linear index threshold-crossing binary choice model subject to a median independence restriction. The proposal exploits an observational equivalence between the model and a probit model with nonparametric heteroskedasticity. This leads to a computationally attractive Bayesian inference procedure in which a Gaussian process forms a conditionally conjugate prior for the natural logarithm of the skedastic function.
■590 ▼aSchool code: 0084.
■650 4▼aStatistics
■653 ▼aBayesian econometrics
■653 ▼aAsymptotic normality
■653 ▼aLog-likelihood function
■653 ▼aBinary choice model
■690 ▼a0501
■690 ▼a0511
■690 ▼a0463
■71020▼aHarvard University▼bEconomics.
■7730 ▼tDissertations Abstracts International▼g86-12B.
■790 ▼a0084
■791 ▼aPh.D.
■792 ▼a2025
■793 ▼aEnglish
■85640▼uhttp://www.riss.kr/pdu/ddodLink.do?id=T17357417▼nKERIS▼z이 자료의 원문은 한국교육학술정보원에서 제공합니다.
