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Essays on the demand for health, health insurance, and medical care.

紀錄類型:	書目-語言資料,手稿 : Monograph/item
正題名/作者:	Essays on the demand for health, health insurance, and medical care./
作者:	Wang, Rui.
面頁冊數:	1 online resource (115 pages)
附註:	Source: Dissertations Abstracts International, Volume: 70-11, Section: A.
Contained By:	Dissertations Abstracts International70-11A.
標題:	Essays. -
電子資源:	click for full text (PQDT)
ISBN:	9781109033571

Essays on the demand for health, health insurance, and medical care.
Wang, Rui.

Essays on the demand for health, health insurance, and medical care. - 1 online resource (115 pages)

Source: Dissertations Abstracts International, Volume: 70-11, Section: A.

Thesis (Ph.D.)--University of Hawai'i at Manoa, 2008.

Includes bibliographical references

This dissertation consists of three essays that address two distinct topics in health economics: (1) optimal health investment and optimal insurance under ex ante moral hazard within a dynamic stochastic human capital framework; and (2) statistical modeling of medical care utilization data based on individual characteristics. The first essay presents a dynamic stochastic model of health investment with health insurance. A representative individual's lifecycle behavior of consumption and investment in health capital is then examined within a continuous-time optimal control framework. With the introduction of health insurance, individual health investment behavior exhibits ex ante moral hazard, and the conditions for optimal health investment under health insurance are derived. A statistical model becomes overfitted when the number of free parameters employed becomes excessive. Models of medical care utilization risk overfitting due to the large number of free parameters involved. The second essay addresses the overfitting problem in the modeling of health care utilization count data by proposing a zero augmented negative binomial model (ZANB). In contrast to the popular two-part model approaches, where the binary and the count processes are modeled with two separate data-generating processes, ZANB conforms the two processes by imposing that parameters from the binary model and parameters from the count model should be linear transformations of each other. This constraint effectively reduces the number of free parameters by nearly one half to mitigate the risk of overfitting. In addition to the theoretical appeals, analyses using survey data confirm the good statistical properties and demonstrate superior prediction accuracy of this new estimation scheme. The third essay proposes a pseudo-full information estimation scheme (PFIE) that promotes model simplicity (and hence parameter stability) at the cost of model specification. PFIE is useful in the modeling of multiple data generating processes (DGP) where parameters from distinct DGP's are correlated. By imposing constraints on parameters based on their correlation, the number of free parameters in the model can be greatly reduced. Essay Three conducts a number of Monte Carlo simulation experiments to numerically examine the statistical properties of PFIE under various parametric assumptions.

Electronic reproduction.
Ann Arbor, Mich. :
ProQuest,
2024

Mode of access: World Wide Web

ISBN: 9781109033571Subjects--Topical Terms:

947995
Essays.
Subjects--Index Terms:

HealthIndex Terms--Genre/Form:

554714
Electronic books.

Essays on the demand for health, health insurance, and medical care.
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504 $a Includes bibliographical references
520 $a This dissertation consists of three essays that address two distinct topics in health economics: (1) optimal health investment and optimal insurance under ex ante moral hazard within a dynamic stochastic human capital framework; and (2) statistical modeling of medical care utilization data based on individual characteristics. The first essay presents a dynamic stochastic model of health investment with health insurance. A representative individual's lifecycle behavior of consumption and investment in health capital is then examined within a continuous-time optimal control framework. With the introduction of health insurance, individual health investment behavior exhibits ex ante moral hazard, and the conditions for optimal health investment under health insurance are derived. A statistical model becomes overfitted when the number of free parameters employed becomes excessive. Models of medical care utilization risk overfitting due to the large number of free parameters involved. The second essay addresses the overfitting problem in the modeling of health care utilization count data by proposing a zero augmented negative binomial model (ZANB). In contrast to the popular two-part model approaches, where the binary and the count processes are modeled with two separate data-generating processes, ZANB conforms the two processes by imposing that parameters from the binary model and parameters from the count model should be linear transformations of each other. This constraint effectively reduces the number of free parameters by nearly one half to mitigate the risk of overfitting. In addition to the theoretical appeals, analyses using survey data confirm the good statistical properties and demonstrate superior prediction accuracy of this new estimation scheme. The third essay proposes a pseudo-full information estimation scheme (PFIE) that promotes model simplicity (and hence parameter stability) at the cost of model specification. PFIE is useful in the modeling of multiple data generating processes (DGP) where parameters from distinct DGP's are correlated. By imposing constraints on parameters based on their correlation, the number of free parameters in the model can be greatly reduced. Essay Three conducts a number of Monte Carlo simulation experiments to numerically examine the statistical properties of PFIE under various parametric assumptions.
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