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Essays on semiparametric cox proportional hazard models

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Title
Essays on semiparametric cox proportional hazard models
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ETD_2172
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http://hdl.rutgers.edu/1782.2/rucore10001600001.ETD.000051930
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eng
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theses
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Topic
Economics
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Topic
Nonparametric statistics
Subject (ID = SBJ-3); (authority = ETD-LCSH)
Topic
Estimation theory
Abstract
In this dissertation I study different versions of the semiparametric proportional hazard duration model and their practical applications under both frequentist and Bayesian econometrics frameworks. I use the unemployment spell data set that is created from the Panel Study of Income Dynamics (PSID).
In Chapter 1 I study the effects of unemployment compensation and other important sociodemographic factors on unemployment duration. Whether duration dependence follows a particular function form is also examined. Discrete, semiparametric, proportional hazard models are used and compared among different specifications. I allow for nonparametric estimation of the effect of time on the unemployment exit rate. Because unobserved individual heterogeneity has the potential to bias the estimation results, we also consider gamma heterogeneity as an additional source of error in the hazard model (i.e., the so called mixed proportional hazard model, MPH). I find that the nonparametric baseline hazard estimations capture very well the shape of the empirical duration, which often does not belong to a specific parametric family; and unemployment insurance and socio-demographic aspects have significant impacts on the unemployment spell.
In the second chapter I test whether different ways to resume work, such as new job and recall, have different duration behaviors. Hence a semiparametric dependent competing risks proportional hazard model is specified. Identifiability of such model is also discussed. By assuming linearity on the baseline hazard at each time interval, I allow for unrestricted correlation between the competing risks. My model guarantees that the unobserved failure occurs later than the observed failure at any possible time point, and censored observations are accommodated explicitly in the model specification. The estimated correlation coefficient suggests that recall duration and new job duration have a positive relationship that may not be negligible. We also find that there is significant difference in the hazard structure of returning to the same employer and a different employer.
Different from the first two chapters, in the third chapter I investigate the ordered probit duration model semiparametrically using the Bayesian Markov Chain Monte Carlo (MCMC) methods. I develop and estimate the model without considering unobserved heterogeneity, and noninformative priors are assumed for both the baseline hazard and regressor parameters. Hybrid Metropolis-Hastings/Gibbs sampler is employed to speed up chain mixture. Convergence of the chains is assessed by the Gelman-Rubin scale reduction factor. Applications on the PSID unemployment duration data demonstrate that the proposed model and estimation method perform well.
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electronic resource
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x, 111 p. : ill.
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Note (type = degree)
Ph.D.
Note (type = bibliography)
Includes bibliographical references (p. 103-110)
Note (type = statement of responsibility)
by Huiying Zhang
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Zhang
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Huiying
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Huiying Zhang
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Tsurumi
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Hiroki
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Hiroki Tsurumi
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Killingsworth
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Mark
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Mark Killingsworth
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Sigman
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Hilary
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Hilary Sigman
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Voicu
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Alex
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Alex Voicu
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Rutgers University
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degree grantor
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Graduate School - New Brunswick
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OriginInfo
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2009
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2009-10
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xx
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Title
Rutgers University Electronic Theses and Dissertations
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ETD
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Title
Graduate School - New Brunswick Electronic Theses and Dissertations
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rucore19991600001
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NjNbRU
Identifier (type = doi)
doi:10.7282/T3J67H2W
Genre (authority = ExL-Esploro)
ETD doctoral
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The author owns the copyright to this work
Copyright
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Copyright protected
Notice
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Open
Reason
Permission or license
Note
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Zhang
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Huiying
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Huiying Zhang
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Rutgers University. Graduate School - New Brunswick
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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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