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Healthcare decisions
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Text
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Title
Healthcare decisions
SubTitle
formulation and application of semiparametric methods
Identifier
ETD_1618
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http://hdl.rutgers.edu/1782.2/rucore10001600001.ETD.000051402
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eng
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theses
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Topic
Economics
Subject
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Topic
Health insurance--Mathematical models
Abstract
Currently, there appears to be a tradeoff between the performance of a semiparametric estimator in finite and large samples. In Chapter 1, we argue that this tradeoff occurs because of the nature of the bias reduction methods that are often employed in implementing these estimators. Accordingly, we develop a bias control mechanism that eliminates this tradeoff so as to ensure that the estimator performs well in finite samples while retaining desirable large sample properties.
Semiparametric models are commonly estimated under a single index assumption. In estimating these models, the consistency of the estimator critically depends on this assumption being correct. Therefore, in Chapter 2, we develop a test of this assumption. We formulate such a test and derive its large sample distribution under the null hypothesis of a single index. To ensure that the test statistic has good size and power properties in finite samples, we formulate a test whose form adapts to the model under the alternative hypothesis. Monte Carlo results confirm that the adaptive feature significantly improves the performance of the test statistic in finite samples.
Studying healthcare decisions poses many empirical challenges. Healthcare utilization and expenditures depend on health insurance and other health related variables. As insurance is a choice variable for the individual, there are potential endogeneity issues. Expenditures are only observed when utilization occurs and hence there is a selection problem. Furthermore, the decision to utilize healthcare and the decision about the level of treatment are determined by different decision makers. In Chapter 3, we study a system of three simultaneous equations: insurance, utilization, and expenditures. To avoid making traditional parametric distributional assumptions, we propose a semiparametric approach based on the previous two chapters. Both parametric and semiparametric approaches are employed in an empirical study using the Medical Expenditure Panel Survey (MEPS) 2005 data. We find that insurance increases the likelihood of seeking healthcare by about 15% points (from about 80% to 95%). We also find that the parametric approach predicts insurance to increase the level of expenditures by 125%; while the semiparametric method predicts an increase of 51%, a number in accord with an important experimental study in the literature.
Semiparametric models are commonly estimated under a single index assumption. In estimating these models, the consistency of the estimator critically depends on this assumption being correct. Therefore, in Chapter 2, we develop a test of this assumption. We formulate such a test and derive its large sample distribution under the null hypothesis of a single index. To ensure that the test statistic has good size and power properties in finite samples, we formulate a test whose form adapts to the model under the alternative hypothesis. Monte Carlo results confirm that the adaptive feature significantly improves the performance of the test statistic in finite samples.
Studying healthcare decisions poses many empirical challenges. Healthcare utilization and expenditures depend on health insurance and other health related variables. As insurance is a choice variable for the individual, there are potential endogeneity issues. Expenditures are only observed when utilization occurs and hence there is a selection problem. Furthermore, the decision to utilize healthcare and the decision about the level of treatment are determined by different decision makers. In Chapter 3, we study a system of three simultaneous equations: insurance, utilization, and expenditures. To avoid making traditional parametric distributional assumptions, we propose a semiparametric approach based on the previous two chapters. Both parametric and semiparametric approaches are employed in an empirical study using the Medical Expenditure Panel Survey (MEPS) 2005 data. We find that insurance increases the likelihood of seeking healthcare by about 15% points (from about 80% to 95%). We also find that the parametric approach predicts insurance to increase the level of expenditures by 125%; while the semiparametric method predicts an increase of 51%, a number in accord with an important experimental study in the literature.
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electronic resource
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vii, 106 p. : ill.
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Supplementary File: dissertation with signed title page
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Ph.D.
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Includes bibliographical references (p. 102-105)
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by Chan Shen
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Shen
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Chan
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1981
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Chan Shen
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Klein
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Roger
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Roger Klein
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Moehling
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Carolyn
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Carolyn Moehling
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Landon-Lane
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John
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John Landon-Lane
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Sambamoorthi
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Usha
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Usha Sambamoorthi
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Rutgers University
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Graduate School - New Brunswick
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2009
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2009-05
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xx
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Rutgers University Electronic Theses and Dissertations
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Graduate School - New Brunswick Electronic Theses and Dissertations
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doi:10.7282/T3G1612C
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ETD doctoral
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Author Agreement License
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I hereby grant to the Rutgers University Libraries and to my school the non-exclusive right to archive, reproduce and distribute my thesis or dissertation, in whole or in part, and/or my abstract, in whole or in part, in and from an electronic format, subject to the release date subsequently stipulated in this submittal form and approved by my school. I represent and stipulate that the thesis or dissertation and its abstract are my original work, that they do not infringe or violate any rights of others, and that I make these grants as the sole owner of the rights to my thesis or dissertation and its abstract. I represent that I have obtained written permissions, when necessary, from the owner(s) of each third party copyrighted matter to be included in my thesis or dissertation and will supply copies of such upon request by my school. I acknowledge that RU ETD and my school will not distribute my thesis or dissertation or its abstract if, in their reasonable judgment, they believe all such rights have not been secured. I acknowledge that I retain ownership rights to the copyright of my work. I also retain the right to use all or part of this thesis or dissertation in future works, such as articles or books.
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