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Essays in Econometrics
Essays in Econometrics
상세정보
- 자료유형
- 학위논문 서양
- 최종처리일시
- 20250211150915
- ISBN
- 9798383161524
- DDC
- 310
- 서명/저자
- Essays in Econometrics
- 발행사항
- [Sl] : New York University, 2024
- 발행사항
- Ann Arbor : ProQuest Dissertations & Theses, 2024
- 형태사항
- 89 p
- 주기사항
- Source: Dissertations Abstracts International, Volume: 85-12, Section: B.
- 주기사항
- Advisor: Manresa, Elena.
- 학위논문주기
- Thesis (Ph.D.)--New York University, 2024.
- 초록/해제
- 요약In the first two integrated chapters of this dissertation, I leverage machine learning techniques to develop novel estimation methodologies for various economic models. The third chapter investigates the interplay between production networks and labor search frictions.The inaugural chapter, titled "Adversarial Method of Moments," jointly authored with Elena Manresa, introduces a fresh estimation approach tailored for models characterized by moment conditions. Inspired in recent developments in Machine Learning, particularly Generative Adversarial Networks, we analyze the asymptotic and finite-sample properties of these estimators and conduct Monte-Carlo simulations to compare their efficiency properties against traditional methods like Generalized Method of Moments (GMM) and Generalized Empirical Likelihood (GEL) estimators. Our findings reveal a bias-variance trade-off inherent in our approach, together with a data-driven procedure to fine-tune our estimator. In the subsequent chapter, "AMM for Simulation-Based Models" we extend the methodology from the preceding chapter to minimum-distance estimation. Notably, we demonstrate that integrating our estimator with a bootstrap technique yields superior results compared to standard methods. Moreover, we test our approach in a macroeconomic DSGE model, where we estimate the parameters by matching the Impulse Response Function of a Structural VAR between the data and the underlying model. Finally, in the third chapter, "Labor Search Frictions in Production Networks," I explore the dynamic interaction between production networks and labor market inefficiencies. By calibrating the model to the US economy, I uncover that the combined effects of these factors substantially account for unemployment and output fluctuations.
- 일반주제명
- Statistics
- 키워드
- Discrete choice
- 키워드
- Econometrics
- 키워드
- Machine learning
- 키워드
- Macroeconomics
- 키워드
- Search theory
- 기타저자
- New York University Economics
- 기본자료저록
- Dissertations Abstracts International. 85-12B.
- 전자적 위치 및 접속
- 로그인 후 원문을 볼 수 있습니다.
MARC
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■00520250211150915
■006m o d
■007cr#unu||||||||
■020 ▼a9798383161524
■035 ▼a(MiAaPQ)AAI30813092
■040 ▼aMiAaPQ▼cMiAaPQ
■0820 ▼a310
■1001 ▼aCigliutti, Ignacio Martin.
■24510▼aEssays in Econometrics
■260 ▼a[Sl]▼bNew York University▼c2024
■260 1▼aAnn Arbor▼bProQuest Dissertations & Theses▼c2024
■300 ▼a89 p
■500 ▼aSource: Dissertations Abstracts International, Volume: 85-12, Section: B.
■500 ▼aAdvisor: Manresa, Elena.
■5021 ▼aThesis (Ph.D.)--New York University, 2024.
■520 ▼aIn the first two integrated chapters of this dissertation, I leverage machine learning techniques to develop novel estimation methodologies for various economic models. The third chapter investigates the interplay between production networks and labor search frictions.The inaugural chapter, titled "Adversarial Method of Moments," jointly authored with Elena Manresa, introduces a fresh estimation approach tailored for models characterized by moment conditions. Inspired in recent developments in Machine Learning, particularly Generative Adversarial Networks, we analyze the asymptotic and finite-sample properties of these estimators and conduct Monte-Carlo simulations to compare their efficiency properties against traditional methods like Generalized Method of Moments (GMM) and Generalized Empirical Likelihood (GEL) estimators. Our findings reveal a bias-variance trade-off inherent in our approach, together with a data-driven procedure to fine-tune our estimator. In the subsequent chapter, "AMM for Simulation-Based Models" we extend the methodology from the preceding chapter to minimum-distance estimation. Notably, we demonstrate that integrating our estimator with a bootstrap technique yields superior results compared to standard methods. Moreover, we test our approach in a macroeconomic DSGE model, where we estimate the parameters by matching the Impulse Response Function of a Structural VAR between the data and the underlying model. Finally, in the third chapter, "Labor Search Frictions in Production Networks," I explore the dynamic interaction between production networks and labor market inefficiencies. By calibrating the model to the US economy, I uncover that the combined effects of these factors substantially account for unemployment and output fluctuations.
■590 ▼aSchool code: 0146.
■650 4▼aStatistics
■653 ▼aDiscrete choice
■653 ▼aEconometrics
■653 ▼aMachine learning
■653 ▼aMacroeconomics
■653 ▼aProduction networks
■653 ▼aSearch theory
■690 ▼a0501
■690 ▼a0463
■690 ▼a0510
■71020▼aNew York University▼bEconomics.
■7730 ▼tDissertations Abstracts International▼g85-12B.
■790 ▼a0146
■791 ▼aPh.D.
■792 ▼a2024
■793 ▼aEnglish
■85640▼uhttp://www.riss.kr/pdu/ddodLink.do?id=T17160130▼nKERIS▼z이 자료의 원문은 한국교육학술정보원에서 제공합니다.
