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Essays on Group Heterogeneity in Panel Data Models- [electronic resource]
Essays on Group Heterogeneity in Panel Data Models- [electronic resource]
Detailed Information
- 자료유형
- 학위논문파일 국외
- 최종처리일시
- 20240214100116
- ISBN
- 9798379751005
- DDC
- 310
- 저자명
- Zhang, Boyuan.
- 서명/저자
- Essays on Group Heterogeneity in Panel Data Models - [electronic resource]
- 발행사항
- [S.l.]: : University of Pennsylvania., 2023
- 발행사항
- Ann Arbor : : ProQuest Dissertations & Theses,, 2023
- 형태사항
- 1 online resource(142 p.)
- 주기사항
- Source: Dissertations Abstracts International, Volume: 84-12, Section: B.
- 주기사항
- Advisor: Diebold, Francis X.;Schorfheide, Frank.
- 학위논문주기
- Thesis (Ph.D.)--University of Pennsylvania, 2023.
- 사용제한주기
- This item must not be sold to any third party vendors.
- 초록/해제
- 요약The assumption of group heterogeneity has become popular in panel data models. Instead of modeling heterogeneity via unit-specific coefficients, the cross-sectional units are assumed to cluster into groups, and within each group, units share the same coefficients. This thesis develops an econometric framework to incorporate prior knowledge of groups, which is considered additional information that does not enter the likelihood function. The prior knowledge aids in clustering units into groups and sharpens the inference of group-specific parameters, particularly when units are not well-separated.In the first chapter, Incorporating Prior Knowledge of Latent Group Structure in Panel Data Models, we develop a constrained Bayesian grouped estimator that exploits researchers' prior beliefs on groups in a form of pairwise constraints, indicating whether a pair of units is likely to belong to the same group or different groups. We propose a prior to incorporate the pairwise constraints with varying degrees of confidence in the panel data models. In the second chapter, Heterogeneous Effect of Income on Democracy, we revisit the relationship between a country's income and its democratic transition. The proposed framework uncovers a group structure with a moderate number of groups, each exhibiting a unique and distinct trajectory toward democracy. Furthermore, we identify heterogeneous income effects on democracy and, contrary to the initial findings, show that a positive income effect persists in some groups of countries, though quantitatively small. In the third chapter, Forecast US CPI Inflation of Sub-Indices, we predict the inflation of the U.S. CPI sub-indices. The results indicate that the proposed predictor generates more precise density forecasts than standard models, which can be primarily attributed to three key features: the nonparametric Bayesian prior, an a priori belief on group structure, and grouped cross-sectional heteroskedasticity.The three chapters are closely interrelated. Chapter one introduces a novel nonparametric Bayesian prior for panel data models, enabling the estimation of group heterogeneity while considering prior information on the underlying group structure. Chapter two delves into the applied question of income's effect on democracy, where researchers have prior knowledge about the group. This serves as an ideal case study to illustrate the improvement of posterior inference through the use of prior group information. Chapter three focuses on forecasting, exploring the benefits of incorporating prior group knowledge into predictions. Collectively, these chapters provide a comprehensive understanding of analyzing group heterogeneity in panel models while incorporating prior knowledge.
- 일반주제명
- Statistics.
- 키워드
- Panel data
- 기타저자
- University of Pennsylvania Economics
- 기본자료저록
- Dissertations Abstracts International. 84-12B.
- 기본자료저록
- Dissertation Abstract International
- 전자적 위치 및 접속
- 로그인 후 원문을 볼 수 있습니다.
MARC
008240612s2023 us |||||||||||||||c||eng d■001000016931775
■00520240214100116
■006m o d
■007cr#unu||||||||
■020 ▼a9798379751005
■035 ▼a(MiAaPQ)AAI30422723
■040 ▼aMiAaPQ▼cMiAaPQ
■0820 ▼a310
■1001 ▼aZhang, Boyuan.
■24510▼aEssays on Group Heterogeneity in Panel Data Models▼h[electronic resource]
■260 ▼a[S.l.]:▼bUniversity of Pennsylvania. ▼c2023
■260 1▼aAnn Arbor :▼bProQuest Dissertations & Theses, ▼c2023
■300 ▼a1 online resource(142 p.)
■500 ▼aSource: Dissertations Abstracts International, Volume: 84-12, Section: B.
■500 ▼aAdvisor: Diebold, Francis X.;Schorfheide, Frank.
■5021 ▼aThesis (Ph.D.)--University of Pennsylvania, 2023.
■506 ▼aThis item must not be sold to any third party vendors.
■520 ▼aThe assumption of group heterogeneity has become popular in panel data models. Instead of modeling heterogeneity via unit-specific coefficients, the cross-sectional units are assumed to cluster into groups, and within each group, units share the same coefficients. This thesis develops an econometric framework to incorporate prior knowledge of groups, which is considered additional information that does not enter the likelihood function. The prior knowledge aids in clustering units into groups and sharpens the inference of group-specific parameters, particularly when units are not well-separated.In the first chapter, Incorporating Prior Knowledge of Latent Group Structure in Panel Data Models, we develop a constrained Bayesian grouped estimator that exploits researchers' prior beliefs on groups in a form of pairwise constraints, indicating whether a pair of units is likely to belong to the same group or different groups. We propose a prior to incorporate the pairwise constraints with varying degrees of confidence in the panel data models. In the second chapter, Heterogeneous Effect of Income on Democracy, we revisit the relationship between a country's income and its democratic transition. The proposed framework uncovers a group structure with a moderate number of groups, each exhibiting a unique and distinct trajectory toward democracy. Furthermore, we identify heterogeneous income effects on democracy and, contrary to the initial findings, show that a positive income effect persists in some groups of countries, though quantitatively small. In the third chapter, Forecast US CPI Inflation of Sub-Indices, we predict the inflation of the U.S. CPI sub-indices. The results indicate that the proposed predictor generates more precise density forecasts than standard models, which can be primarily attributed to three key features: the nonparametric Bayesian prior, an a priori belief on group structure, and grouped cross-sectional heteroskedasticity.The three chapters are closely interrelated. Chapter one introduces a novel nonparametric Bayesian prior for panel data models, enabling the estimation of group heterogeneity while considering prior information on the underlying group structure. Chapter two delves into the applied question of income's effect on democracy, where researchers have prior knowledge about the group. This serves as an ideal case study to illustrate the improvement of posterior inference through the use of prior group information. Chapter three focuses on forecasting, exploring the benefits of incorporating prior group knowledge into predictions. Collectively, these chapters provide a comprehensive understanding of analyzing group heterogeneity in panel models while incorporating prior knowledge.
■590 ▼aSchool code: 0175.
■650 4▼aStatistics.
■653 ▼aPanel data
■653 ▼aGroup heterogeneity
■653 ▼aUnit-specific coefficients
■653 ▼aEconometric framework
■690 ▼a0501
■690 ▼a0463
■71020▼aUniversity of Pennsylvania▼bEconomics.
■7730 ▼tDissertations Abstracts International▼g84-12B.
■773 ▼tDissertation Abstract International
■790 ▼a0175
■791 ▼aPh.D.
■792 ▼a2023
■793 ▼aEnglish
■85640▼uhttp://www.riss.kr/pdu/ddodLink.do?id=T16931775▼nKERIS▼z이 자료의 원문은 한국교육학술정보원에서 제공합니다.
■980 ▼a202402▼f2024
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