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Essays on Group Heterogeneity in Panel Data Models- [electronic resource]
Essays on Group Heterogeneity in Panel Data Models - [electronic resource]
Essays on Group Heterogeneity in Panel Data Models- [electronic resource]

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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
키워드  
Group heterogeneity
키워드  
Unit-specific coefficients
키워드  
Econometric framework
기타저자  
University of Pennsylvania Economics
기본자료저록  
Dissertations Abstracts International. 84-12B.
기본자료저록  
Dissertation Abstract International
전자적 위치 및 접속  
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■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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