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Understanding Macroeconomic Dynamics: Big-Data Forecasting and the Effects of Oil Price Shocks
Understanding Macroeconomic Dynamics: Big-Data Forecasting and the Effects of Oil Price Shocks
Detailed Information
- 자료유형
- 학위논문 서양
- 최종처리일시
- 20250211152122
- ISBN
- 9798383666579
- DDC
- 330
- 저자명
- Konduri, Teja.
- 서명/저자
- Understanding Macroeconomic Dynamics: Big-Data Forecasting and the Effects of Oil Price Shocks
- 발행사항
- [Sl] : University of Notre Dame, 2024
- 발행사항
- Ann Arbor : ProQuest Dissertations & Theses, 2024
- 형태사항
- 239 p
- 주기사항
- Source: Dissertations Abstracts International, Volume: 86-02, Section: A.
- 주기사항
- Advisor: Baumeister, Christiane.
- 학위논문주기
- Thesis (Ph.D.)--University of Notre Dame, 2024.
- 초록/해제
- 요약This dissertation comprises three chapters. The first chapter compares macroeconomic forecasts of various machine learning models. The subsequent two chapters evaluate the response of unemployment and monetary policy to various oil price shocks.The first chapter evaluates the performance of an extensive set of machine learning algorithms in forecasting macroeconomic variables relative to benchmark econometric models. We conduct a pseudo-out-of-sample forecast for fifteen real, nominal, and financial variables. Machine learning models outperform the benchmark in forecasting real variables, attributed to their ability to handle nonlinearities, but perform worse in forecasting nominal and financial variables. They beat the benchmark during high volatility episodes, like recessions and the COVID-19 pandemic. Dimension reduction models frequently appear in the top five most accurate models for real variables, especially at longer horizons.In the second chapter, we utilize local projections to investigate the impact of structural oil price shocks on unemployment rates and spells across the United States, emphasizing both national and state-level variations. Oil supply shocks lead to long-run increases in the national unemployment rate, incidence, and short-term unemployment. In contrast, economic activity shocks reduce all unemployment rates and spells, especially in oil-producing states. Consumption demand shocks have minimal impact on unemployment rates and durations, while inventory demand shocks show only temporary effects on durations.The third chapter uses local projections to investigate the macroeconomic and monetary policy responses to adverse oil supply shocks. The Federal Reserve raises interest rates twice: on impact and ten months after the shock to counter ongoing high inflation. A net oil exporter, Canada raises interest rates sharply in response to the shock to counter inflation. Switzerland initially maintains steady interest rates to prevent Swiss Franc appreciation, followed by gradual rate increases to manage inflation as the exchange rate stabilizes. Despite these efforts, inflation remains high in Switzerland.
- 일반주제명
- Forecasting
- 일반주제명
- Estimates
- 일반주제명
- Advisors
- 일반주제명
- Confidence intervals
- 일반주제명
- Light
- 일반주제명
- Oil consumption
- 일반주제명
- Comparative analysis
- 키워드
- Big data
- 키워드
- Forecasting
- 키워드
- Monetary policy
- 키워드
- Oil price shocks
- 기타저자
- University of Notre Dame Economics
- 기본자료저록
- Dissertations Abstracts International. 86-02A.
- 전자적 위치 및 접속
- 로그인 후 원문을 볼 수 있습니다.
MARC
008250123s2024 us c eng d■001000017163002
■00520250211152122
■006m o d
■007cr#unu||||||||
■020 ▼a9798383666579
■035 ▼a(MiAaPQ)AAI31481817
■040 ▼aMiAaPQ▼cMiAaPQ
■0820 ▼a330
■1001 ▼aKonduri, Teja.▼0(orcid)0009-0008-8640-7501
■24510▼aUnderstanding Macroeconomic Dynamics: Big-Data Forecasting and the Effects of Oil Price Shocks
■260 ▼a[Sl]▼bUniversity of Notre Dame▼c2024
■260 1▼aAnn Arbor▼bProQuest Dissertations & Theses▼c2024
■300 ▼a239 p
■500 ▼aSource: Dissertations Abstracts International, Volume: 86-02, Section: A.
■500 ▼aAdvisor: Baumeister, Christiane.
■5021 ▼aThesis (Ph.D.)--University of Notre Dame, 2024.
■520 ▼aThis dissertation comprises three chapters. The first chapter compares macroeconomic forecasts of various machine learning models. The subsequent two chapters evaluate the response of unemployment and monetary policy to various oil price shocks.The first chapter evaluates the performance of an extensive set of machine learning algorithms in forecasting macroeconomic variables relative to benchmark econometric models. We conduct a pseudo-out-of-sample forecast for fifteen real, nominal, and financial variables. Machine learning models outperform the benchmark in forecasting real variables, attributed to their ability to handle nonlinearities, but perform worse in forecasting nominal and financial variables. They beat the benchmark during high volatility episodes, like recessions and the COVID-19 pandemic. Dimension reduction models frequently appear in the top five most accurate models for real variables, especially at longer horizons.In the second chapter, we utilize local projections to investigate the impact of structural oil price shocks on unemployment rates and spells across the United States, emphasizing both national and state-level variations. Oil supply shocks lead to long-run increases in the national unemployment rate, incidence, and short-term unemployment. In contrast, economic activity shocks reduce all unemployment rates and spells, especially in oil-producing states. Consumption demand shocks have minimal impact on unemployment rates and durations, while inventory demand shocks show only temporary effects on durations.The third chapter uses local projections to investigate the macroeconomic and monetary policy responses to adverse oil supply shocks. The Federal Reserve raises interest rates twice: on impact and ten months after the shock to counter ongoing high inflation. A net oil exporter, Canada raises interest rates sharply in response to the shock to counter inflation. Switzerland initially maintains steady interest rates to prevent Swiss Franc appreciation, followed by gradual rate increases to manage inflation as the exchange rate stabilizes. Despite these efforts, inflation remains high in Switzerland.
■590 ▼aSchool code: 0165.
■650 4▼aForecasting
■650 4▼aEstimates
■650 4▼aAdvisors
■650 4▼aConfidence intervals
■650 4▼aLight
■650 4▼aOil consumption
■650 4▼aComparative analysis
■653 ▼aBig data
■653 ▼aForecasting
■653 ▼aLocal projections
■653 ▼aMonetary policy
■653 ▼aOil price shocks
■653 ▼aUnemployment rate
■690 ▼a0501
■71020▼aUniversity of Notre Dame▼bEconomics.
■7730 ▼tDissertations Abstracts International▼g86-02A.
■790 ▼a0165
■791 ▼aPh.D.
■792 ▼a2024
■793 ▼aEnglish
■85640▼uhttp://www.riss.kr/pdu/ddodLink.do?id=T17163002▼nKERIS▼z이 자료의 원문은 한국교육학술정보원에서 제공합니다.
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