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Essays in Time Series Econometrics With Applications to Public Health /

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Essays in Time Series Econometrics With Applications to Public Health /Shuofan Zhang.
作者:	Zhang, Shuofan,
面頁冊數:	1 electronic resource (101 pages)
附註:	Source: Dissertations Abstracts International, Volume: 86-11, Section: B.
附註:	Advisors: Hansen, Peter R. Committee members: Anderson, Heather; Babii, Andrii; Kedagni, Desire; Verdier, Valentin.
Contained By:	Dissertations Abstracts International86-11B.
標題:	Public health.
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=31937782
ISBN:	9798315705086

Essays in Time Series Econometrics With Applications to Public Health /
Zhang, Shuofan,

Essays in Time Series Econometrics With Applications to Public Health /Shuofan Zhang. - 1 electronic resource (101 pages)

Source: Dissertations Abstracts International, Volume: 86-11, Section: B.

This dissertation comprises three independent but thematically connected essays at the intersection of health economics, econometrics, and time series analysis. Each chapter addresses a pressing public health issue through rigorous empirical investigation, with a focus on improving the measurement and interpretation of dynamic, policy-relevant data. The first chapter analyzes the temporal evolution of COVID-19 vaccine efficacy by extracting time series data from clinical trial reports for the Pfizer, Moderna, and Janssen vaccines. I estimate dynamic vaccine protection profiles and construct confidence intervals for these trajectories. The findings highlight not only the heterogeneity across vaccines but also their temporal patterns of waning efficacy, providing a framework that can be extended to future vaccine monitoring and public health planning. The second chapter addresses the bias introduced by variation in COVID-19 testing behavior when estimating the true course of the pandemic. Using a Generalized Autoregressive Score (GAS) model, I construct an adjusted measure of case growth that accounts for changes in testing intensity. By modeling confirmed cases as a binomial process conditional on testing volume, this method produces smoothed, more accurate estimates of infection trends that are robust to changes in testing policy. The model's validity is supported through alignment with independent estimates from government studies. The third chapter investigates the evolving relationship between temperature and mortality in the United States, with a particular focus on seasonal adaptation. Leveraging long-run mortality and weather data, I evaluate the extent to which populations have adapted to temperature extremes across decades. The analysis reveals significant adaptation to cold temperatures after 1960, suggesting improvements in housing, heating, and health systems. This work contributes to the growing literature on climate resilience and offers insights into how socioeconomic and infrastructural development mediate environmental health risks. Together, these essays offer novel empirical approaches to understanding the dynamic interaction between health outcomes, behavior, and policy, with implications for both current public health responses and future economic resilience.

English

ISBN: 9798315705086Subjects--Topical Terms:

209444
Public health.
Subjects--Index Terms:

Econometrics

Essays in Time Series Econometrics With Applications to Public Health /
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520 $a This dissertation comprises three independent but thematically connected essays at the intersection of health economics, econometrics, and time series analysis. Each chapter addresses a pressing public health issue through rigorous empirical investigation, with a focus on improving the measurement and interpretation of dynamic, policy-relevant data. The first chapter analyzes the temporal evolution of COVID-19 vaccine efficacy by extracting time series data from clinical trial reports for the Pfizer, Moderna, and Janssen vaccines. I estimate dynamic vaccine protection profiles and construct confidence intervals for these trajectories. The findings highlight not only the heterogeneity across vaccines but also their temporal patterns of waning efficacy, providing a framework that can be extended to future vaccine monitoring and public health planning. The second chapter addresses the bias introduced by variation in COVID-19 testing behavior when estimating the true course of the pandemic. Using a Generalized Autoregressive Score (GAS) model, I construct an adjusted measure of case growth that accounts for changes in testing intensity. By modeling confirmed cases as a binomial process conditional on testing volume, this method produces smoothed, more accurate estimates of infection trends that are robust to changes in testing policy. The model's validity is supported through alignment with independent estimates from government studies. The third chapter investigates the evolving relationship between temperature and mortality in the United States, with a particular focus on seasonal adaptation. Leveraging long-run mortality and weather data, I evaluate the extent to which populations have adapted to temperature extremes across decades. The analysis reveals significant adaptation to cold temperatures after 1960, suggesting improvements in housing, heating, and health systems. This work contributes to the growing literature on climate resilience and offers insights into how socioeconomic and infrastructural development mediate environmental health risks. Together, these essays offer novel empirical approaches to understanding the dynamic interaction between health outcomes, behavior, and policy, with implications for both current public health responses and future economic resilience.
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590 $a School code: 0153
650 4 $a Public health. $3 209444
653 $a Econometrics
653 $a Time series
653 $a COVID-19
653 $a Generalized Autoregressive Score
653 $a Economic resilience
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690 $a 0769
690 $a 0573
710 2 $a The University of North Carolina at Chapel Hill. $b Economics. $3 603219
720 1 $a Hansen, Peter R. $e degree supervisor.
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791 $a Ph.D.
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