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Essays on Design of Applied Economics Studies
Essays on Design of Applied Economics Studies
상세정보
- 자료유형
- 학위논문 서양
- 최종처리일시
- 20250211151950
- ISBN
- 9798383173183
- DDC
- 330.015
- 서명/저자
- Essays on Design of Applied Economics Studies
- 발행사항
- [Sl] : Columbia University, 2024
- 발행사항
- Ann Arbor : ProQuest Dissertations & Theses, 2024
- 형태사항
- 151 p
- 주기사항
- Source: Dissertations Abstracts International, Volume: 85-12, Section: A.
- 주기사항
- Advisor: Shrader, Jeffrey G.
- 학위논문주기
- Thesis (Ph.D.)--Columbia University, 2024.
- 초록/해제
- 요약Applied economics studies target effects that can be relatively small. This dissertation delves into some statistical obstacles to the accurate estimation of such effects, with a particular focus on the concepts of statistical power and exaggeration-imprecise studies tend to produce inflated estimates of the effect of interest. It explores implications of low power and exaggeration that are specific to applied economics studies and their design. Through the example of studies on the acute health effects of air pollution, the first chapter identifies tangible drivers of exaggeration that extend beyond small effects and a limited sample size. This analysis uncovers an overarching mechanism, studied in Chapter 2, that induces exaggeration when using causal identification strategies. This subsequent chapter emphasizes that causal approaches only focus on a subset of the variation-the exogenous part-reducing the precision of the study and increasing risks of exaggeration. The final chapter further broadens the discussion to analyze design choices in light of the multiple goals of causal inference studies; these studies aim not only to identify an average effect but also differentiated effects across subgroups, as well as producing insights that extend beyond the population considered. Overall, this dissertation underlines the manifold implications of design choices on non-experimental economic studies, with the aim of contributing to more accurate estimations of effects to better inform policymaking.
- 키워드
- Causal inference
- 키워드
- Empirical design
- 기타저자
- Columbia University Sustainable Development
- 기본자료저록
- Dissertations Abstracts International. 85-12A.
- 전자적 위치 및 접속
- 로그인 후 원문을 볼 수 있습니다.
MARC
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■006m o d
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■020 ▼a9798383173183
■035 ▼a(MiAaPQ)AAI31328271
■040 ▼aMiAaPQ▼cMiAaPQ
■0820 ▼a330.015
■1001 ▼aBagilet, Vincent.
■24510▼aEssays on Design of Applied Economics Studies
■260 ▼a[Sl]▼bColumbia University▼c2024
■260 1▼aAnn Arbor▼bProQuest Dissertations & Theses▼c2024
■300 ▼a151 p
■500 ▼aSource: Dissertations Abstracts International, Volume: 85-12, Section: A.
■500 ▼aAdvisor: Shrader, Jeffrey G.
■5021 ▼aThesis (Ph.D.)--Columbia University, 2024.
■520 ▼aApplied economics studies target effects that can be relatively small. This dissertation delves into some statistical obstacles to the accurate estimation of such effects, with a particular focus on the concepts of statistical power and exaggeration-imprecise studies tend to produce inflated estimates of the effect of interest. It explores implications of low power and exaggeration that are specific to applied economics studies and their design. Through the example of studies on the acute health effects of air pollution, the first chapter identifies tangible drivers of exaggeration that extend beyond small effects and a limited sample size. This analysis uncovers an overarching mechanism, studied in Chapter 2, that induces exaggeration when using causal identification strategies. This subsequent chapter emphasizes that causal approaches only focus on a subset of the variation-the exogenous part-reducing the precision of the study and increasing risks of exaggeration. The final chapter further broadens the discussion to analyze design choices in light of the multiple goals of causal inference studies; these studies aim not only to identify an average effect but also differentiated effects across subgroups, as well as producing insights that extend beyond the population considered. Overall, this dissertation underlines the manifold implications of design choices on non-experimental economic studies, with the aim of contributing to more accurate estimations of effects to better inform policymaking.
■590 ▼aSchool code: 0054.
■653 ▼aApplied economics
■653 ▼aCausal inference
■653 ▼aEmpirical design
■653 ▼aStatistical power
■690 ▼a0501
■690 ▼a0438
■690 ▼a0511
■71020▼aColumbia University▼bSustainable Development.
■7730 ▼tDissertations Abstracts International▼g85-12A.
■790 ▼a0054
■791 ▼aPh.D.
■792 ▼a2024
■793 ▼aEnglish
■85640▼uhttp://www.riss.kr/pdu/ddodLink.do?id=T17162247▼nKERIS▼z이 자료의 원문은 한국교육학술정보원에서 제공합니다.
