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Vanderbilt University
1.
Fu, Lingjun.
Conditional Associations with Big Data: Estimating Adjusted Rank Correlations in the Electronic Health Record.
Degree: MS, Biostatistics, 2017, Vanderbilt University
URL: http://hdl.handle.net/1803/12927 
► In this thesis, we apply and adapt a new method to assess conditional associations in a large dataset from the Vanderbilt University Medical Center Electronic…
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▼ In this thesis, we apply and adapt a new method to assess conditional associations in a large dataset from the
Vanderbilt University Medical Center Electronic Health Record (EHR). We estimate pairwise rank correlations among disease status and lab values in the EHR after adjusting for demographical information. Our covariate-adjusted rank correlations involve fitting cumulative probability models (CPMs), extracting probability-scale residuals (PSRs) from these models, and computing the sample correlation between PSRs for different outcomes. This approach is rank-based, robust, and applicable to a variety of data types. Computational challenges arise with large datasets, particularly when we apply these methods to continuous outcome variables such as most lab values; we propose some workaround solutions. We present our results with estimates and confidence intervals for the partial Spearman’s rank correlations among all pairwise combinations of the most frequent 250 ICD codes and 50 lab results among 472,570 patients with data in the EHR. We
also present results stratified by sex and diabetes status, demonstrating how to assess for differences in correlations between different population strata.
Advisors/Committee Members: Matthew Shotwell (committee member), Bryan Shepherd (Committee Chair).
Subjects/Keywords: Big Data; Adjusted Rank Correlation; Electronic Health Record
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APA (6th Edition):
Fu, L. (2017). Conditional Associations with Big Data: Estimating Adjusted Rank Correlations in the Electronic Health Record. (Thesis). Vanderbilt University. Retrieved from http://hdl.handle.net/1803/12927
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
Chicago Manual of Style (16th Edition):
Fu, Lingjun. “Conditional Associations with Big Data: Estimating Adjusted Rank Correlations in the Electronic Health Record.” 2017. Thesis, Vanderbilt University. Accessed March 08, 2021.
http://hdl.handle.net/1803/12927.
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
MLA Handbook (7th Edition):
Fu, Lingjun. “Conditional Associations with Big Data: Estimating Adjusted Rank Correlations in the Electronic Health Record.” 2017. Web. 08 Mar 2021.
Vancouver:
Fu L. Conditional Associations with Big Data: Estimating Adjusted Rank Correlations in the Electronic Health Record. [Internet] [Thesis]. Vanderbilt University; 2017. [cited 2021 Mar 08].
Available from: http://hdl.handle.net/1803/12927.
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
Council of Science Editors:
Fu L. Conditional Associations with Big Data: Estimating Adjusted Rank Correlations in the Electronic Health Record. [Thesis]. Vanderbilt University; 2017. Available from: http://hdl.handle.net/1803/12927
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
Vanderbilt University
2.
Wanga, Valentine Adhiambo.
Residual-based Test of Conditional Association between Continuous and Ordinal Variables with Application to Genome-wide Association Studies.
Degree: MS, Biostatistics, 2014, Vanderbilt University
URL: http://hdl.handle.net/1803/12859
► The discovery of genes linked with a large array of diseases has been accelerated by genome-wide association studies (GWAS), in which genetic variants in different…
(more)
▼ The discovery of genes linked with a large array of diseases has been accelerated by genome-wide association studies (GWAS), in which genetic variants in different individuals are examined for relationship with a specified phenotype. Most GWAS analyses require modeling the association between single nucleotide polymorphisms (SNPs) and the outcome of interest as additive, dominant, or recessive. In general, this relationship is not known. The genotypes of a marker can be regarded as ordered categorical. An additive model assumes linearity, and approaches that categorize the data ignore order information, resulting in loss of power. Therefore, a method that only assumes a monotonic relationship between SNPs and the outcome of interest may be more robust and powerful than standard approaches. In this thesis, we explore the use of such a method using pharmacogenomics data from a clinical trial that randomized 1858 HIV-infected patients to one of four antiretroviral regimen combinations (tenofovir+efavirenz, tenofovir+atazanavir, abacavir+efaverinz, and abacavir+atazanavir). We are specifically interested in detecting SNPs that are associated with tenofovir clearance and creatinine clearance. We assess the performance of the new method versus the additive, dominant, and recessive models via simulation studies and real data analyses, and compare and contrast findings.
Advisors/Committee Members: Chun Li (Committee Chair), Bryan Shepherd (Committee Chair).
Subjects/Keywords: categorical.; additive; recessive; tenofovir; HIV; pharmacokinetics; creatinine clearance; CoCoBOT; dominant
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APA (6th Edition):
Wanga, V. A. (2014). Residual-based Test of Conditional Association between Continuous and Ordinal Variables with Application to Genome-wide Association Studies. (Thesis). Vanderbilt University. Retrieved from http://hdl.handle.net/1803/12859
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
Chicago Manual of Style (16th Edition):
Wanga, Valentine Adhiambo. “Residual-based Test of Conditional Association between Continuous and Ordinal Variables with Application to Genome-wide Association Studies.” 2014. Thesis, Vanderbilt University. Accessed March 08, 2021.
http://hdl.handle.net/1803/12859.
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
MLA Handbook (7th Edition):
Wanga, Valentine Adhiambo. “Residual-based Test of Conditional Association between Continuous and Ordinal Variables with Application to Genome-wide Association Studies.” 2014. Web. 08 Mar 2021.
Vancouver:
Wanga VA. Residual-based Test of Conditional Association between Continuous and Ordinal Variables with Application to Genome-wide Association Studies. [Internet] [Thesis]. Vanderbilt University; 2014. [cited 2021 Mar 08].
Available from: http://hdl.handle.net/1803/12859.
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
Council of Science Editors:
Wanga VA. Residual-based Test of Conditional Association between Continuous and Ordinal Variables with Application to Genome-wide Association Studies. [Thesis]. Vanderbilt University; 2014. Available from: http://hdl.handle.net/1803/12859
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
Vanderbilt University
3.
Chen, Heng.
Essays on Wavelet Estimation of the Local Average Treatment Effect.
Degree: PhD, Economics, 2012, Vanderbilt University
URL: http://hdl.handle.net/1803/12442
► The first chapter of my dissertation makes two main contributions to the econometrics literature on program evaluation. First, we show that under very mild conditions,…
(more)
▼ The first chapter of my dissertation makes two main contributions to the econometrics literature on program evaluation. First, we show that under very mild conditions, a policy parameter, the local average treatment effect (LATE), is identified in a class of switching regime models. The identification is achieved through either a discontinuity or a kink in the incentive assignment mechanism, depending on which the agent selects the treatment according to a threshold-crossing model. In contrast to Lee (2008) and Card, Lee, and Pei (2009), we allow for not only a (possibly) endogenous observable covariate but also a (possibly) endogenous unobservable covariate to affect program participation. Second, we introduce local constant wavelet estimators of the LATE for both discontinuous and kink incentive assignment mechanisms and establish their asymptotic properties. The finite sample performances of our local constant wavelet estimators are examined through a simulation study.
In the second chapter, we introduce another new class of jump size estimators in a semiparametric mean regression model and apply it to the estimation of the LATE. We refer to members of this class as local polynomial wavelet estimators, and show that all existing jump size estimators, including estimators constructed from differencing two nonparametric estimators and partial linear estimators, belong to the class. We establish asymptotic properties of local polynomial wavelet estimators, and show that they attain the optimal convergence rate even under the presence of slope or higher-order derivative discontinuities. In addition to estimating jump sizes in level, our method automatically leads to estimators of jump sizes in both slope and higher-order derivatives. The finite sample performance of local polynomial wavelet estimators is investigated.
The third chapter provides an intuitive two-step procedure for estimating sizes of the discontinuities in the nonparametric median model, instead of the ones in the semi/non-parametric mean model (Chapters 1 and 2). Our first step is to approximate the original discontinuous median model with the discontinuous mean model by local medians transformation (Zhou, 2006). The second step is to carry out local polynomial wavelets estimators (Chapter 2) for the resulting discontinuous mean model. Unlike the check function approach (Koenker, 2005), our two-step method is (1) directly coming from the least squared loss function instead of the check loss function, thus computationally efficient; (2) asymptotically normal; (3) having the optimal rate of convergence and robust to heavy tailed error distributions which may not even possess variances or means.
Advisors/Committee Members: Tong Li (committee member), Mototsugu Shintani (committee member), Bryan Shepherd (committee member), Yanqin Fan (Committee Chair).
Subjects/Keywords: local polynomial; kink size; jump size; wavelet; local medians transformation
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APA (6th Edition):
Chen, H. (2012). Essays on Wavelet Estimation of the Local Average Treatment Effect. (Doctoral Dissertation). Vanderbilt University. Retrieved from http://hdl.handle.net/1803/12442
Chicago Manual of Style (16th Edition):
Chen, Heng. “Essays on Wavelet Estimation of the Local Average Treatment Effect.” 2012. Doctoral Dissertation, Vanderbilt University. Accessed March 08, 2021.
http://hdl.handle.net/1803/12442.
MLA Handbook (7th Edition):
Chen, Heng. “Essays on Wavelet Estimation of the Local Average Treatment Effect.” 2012. Web. 08 Mar 2021.
Vancouver:
Chen H. Essays on Wavelet Estimation of the Local Average Treatment Effect. [Internet] [Doctoral dissertation]. Vanderbilt University; 2012. [cited 2021 Mar 08].
Available from: http://hdl.handle.net/1803/12442.
Council of Science Editors:
Chen H. Essays on Wavelet Estimation of the Local Average Treatment Effect. [Doctoral Dissertation]. Vanderbilt University; 2012. Available from: http://hdl.handle.net/1803/12442
Vanderbilt University
4.
Huo, Xi.
A Disease Age Structured Model of Epidemic Population Dynamics with Public Health Interventions.
Degree: PhD, Mathematics, 2014, Vanderbilt University
URL: http://hdl.handle.net/1803/12673
► The work in this dissertation is about modeling the spread of an infectious disease in a closed community with two basic public health interventions: (i)…
(more)
▼ The work in this dissertation is about modeling the spread of an infectious disease in a closed community with two basic public health interventions: (i) identifying and isolating symptomatic cases, and (ii) tracing and quarantine of the contacts of identified infectives. Our aim is to evaluate the efficacy of tracing and quarantine strategies which are believed to be an important aspect of controlling an outbreak of emerging or re-emerging infectious diseases. The model is applicable in both emerging epidemics that require isolation, tracing, and quarantine, such as H1N1, SARS (severe acute respiratory syndrome), and influenzas, and re-emerging epidemics that requires isolation and certain vaccination strategies, such as a smallpox bioterrorist attack. Moreover, our model can be applied as a rational basis for decision makers to guide interventions and deploy public health resources in future epidemics.
Advisors/Committee Members: Alexander Powell (committee member), Bryan Shepherd (committee member), Douglas Hardin (committee member), Philip Crooke (committee member), Glenn Webb (Committee Chair).
Subjects/Keywords: partial differential equations; mathematical biology; epidemic disease; public health control; quarantine; contact tracing
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APA (6th Edition):
Huo, X. (2014). A Disease Age Structured Model of Epidemic Population Dynamics with Public Health Interventions. (Doctoral Dissertation). Vanderbilt University. Retrieved from http://hdl.handle.net/1803/12673
Chicago Manual of Style (16th Edition):
Huo, Xi. “A Disease Age Structured Model of Epidemic Population Dynamics with Public Health Interventions.” 2014. Doctoral Dissertation, Vanderbilt University. Accessed March 08, 2021.
http://hdl.handle.net/1803/12673.
MLA Handbook (7th Edition):
Huo, Xi. “A Disease Age Structured Model of Epidemic Population Dynamics with Public Health Interventions.” 2014. Web. 08 Mar 2021.
Vancouver:
Huo X. A Disease Age Structured Model of Epidemic Population Dynamics with Public Health Interventions. [Internet] [Doctoral dissertation]. Vanderbilt University; 2014. [cited 2021 Mar 08].
Available from: http://hdl.handle.net/1803/12673.
Council of Science Editors:
Huo X. A Disease Age Structured Model of Epidemic Population Dynamics with Public Health Interventions. [Doctoral Dissertation]. Vanderbilt University; 2014. Available from: http://hdl.handle.net/1803/12673
Vanderbilt University
5.
Giganti, Mark Joseph.
Statistical Methods for the Analysis of Error-Prone Electronic Health Records: Impact of Source Data Verification, Time Discretized Multiple Imputation, and Variance Estimation with Incompatible Imputation and Analysis Models.
Degree: PhD, Biostatistics, 2018, Vanderbilt University
URL: http://hdl.handle.net/1803/13990
► Observational data from electronic health records (EHRs) are prone to errors which are often correlated across multiple variables. One strategy to assess EHR data quality…
(more)
▼ Observational data from electronic health records (EHRs) are prone to errors which are often correlated across multiple variables. One strategy to assess EHR data quality is to compare the research study data to the original source document for a subset of records and document discrepancies. Given the resource-intensiveness of this source data verification (SDV), it is imperative to be able to justify its continued implementation. Using a data audit from an international HIV setting as a practical example, I propose a framework for assessing the impact of audits on study results and illustrate its implementation. Given the discrepancies in the originally collected data are substantial enough to impact epidemiological inferences, I propose a method to obtain unbiased and efficient estimates in time-to-event analyses while incorporating both the original error-prone data for all subjects and the audited data for the subsample of subjects. This time-discretized modeling and imputation (TDMI) approach uses discrete time models built in a validation sample to multiply impute covariate and outcome values in the remaining unvalidated records. Imputation variances estimates were calculated using an approached proposed by Robins and Wang (2000) that allows for incompatibility between imputation and analysis models. We provide a tutorial for calculating this imputation variance estimator using multiple examples and providing comprehensive R code.
Advisors/Committee Members: Peter Rebeiro (committee member), Qingxia (Cindy) Chen (committee member), Bryan Shepherd (committee member), Jonathan Schildcrout (Committee Chair).
Subjects/Keywords: electronic health records; missing data; time-to-event outcomes; measurement error; variance estimation
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APA (6th Edition):
Giganti, M. J. (2018). Statistical Methods for the Analysis of Error-Prone Electronic Health Records: Impact of Source Data Verification, Time Discretized Multiple Imputation, and Variance Estimation with Incompatible Imputation and Analysis Models. (Doctoral Dissertation). Vanderbilt University. Retrieved from http://hdl.handle.net/1803/13990
Chicago Manual of Style (16th Edition):
Giganti, Mark Joseph. “Statistical Methods for the Analysis of Error-Prone Electronic Health Records: Impact of Source Data Verification, Time Discretized Multiple Imputation, and Variance Estimation with Incompatible Imputation and Analysis Models.” 2018. Doctoral Dissertation, Vanderbilt University. Accessed March 08, 2021.
http://hdl.handle.net/1803/13990.
MLA Handbook (7th Edition):
Giganti, Mark Joseph. “Statistical Methods for the Analysis of Error-Prone Electronic Health Records: Impact of Source Data Verification, Time Discretized Multiple Imputation, and Variance Estimation with Incompatible Imputation and Analysis Models.” 2018. Web. 08 Mar 2021.
Vancouver:
Giganti MJ. Statistical Methods for the Analysis of Error-Prone Electronic Health Records: Impact of Source Data Verification, Time Discretized Multiple Imputation, and Variance Estimation with Incompatible Imputation and Analysis Models. [Internet] [Doctoral dissertation]. Vanderbilt University; 2018. [cited 2021 Mar 08].
Available from: http://hdl.handle.net/1803/13990.
Council of Science Editors:
Giganti MJ. Statistical Methods for the Analysis of Error-Prone Electronic Health Records: Impact of Source Data Verification, Time Discretized Multiple Imputation, and Variance Estimation with Incompatible Imputation and Analysis Models. [Doctoral Dissertation]. Vanderbilt University; 2018. Available from: http://hdl.handle.net/1803/13990
Vanderbilt University
6.
Kasongo, Webster.
Age-Period-Cohort and Educational Attainment Effects on HIV Prevalence in Zambian Pregnant Women, 1994 through 2011.
Degree: PhD, Epidemiology, 2013, Vanderbilt University
URL: http://hdl.handle.net/1803/13829
► Age-Period-Cohort and Educational Attainment Effects on HIV Prevalence in Zambian Pregnant Women, 1994 through 2011 Webster Kasongo Dissertation under the direction of Professor Sten H.…
(more)
▼ Age-Period-Cohort and Educational Attainment Effects on HIV Prevalence in Zambian Pregnant Women, 1994 through 2011 Webster Kasongo
Dissertation under the direction of Professor Sten H. Vermund Sub-Saharan Africa, where Zambia is situated, accounted for 69% of the 34 million people living with HIV worldwide. An estimated one million people were living with HIV in Zambia in 2011 compared to 1.3 million people in the United States of America, with 24 times the population of Zambia. Prior research has suggested declining HIV prevalence trends in Zambia, but whether the decline in HIV prevalence has been influenced by age, period or cohort effects has not been examined contemporaneously. Birth cohort and period variations in HIV prevalence may signal changes in distribution of risk factors for HIV infection, and analyses of age, period and birth cohort’s influence on HIV prevalence may provide key information for focusing HIV interventions.
HIV sentinel surveillance data sourced cross-sectionally, from pregnant who sought antenatal care in 1994, 1998, 2002, 2004, 2006, 2008 and 2011 in Zambia were analyzed to describe HIV prevalence trends in 15 to 24 year olds; to examine association of educational attainments with HIV prevalence; and to investigate age, period and birth cohort effects on HIV prevalence among 15 to 44 year-olds using the cross-classified random effect model proposed by Yang and Land (2006).
HIV prevalence declined from 27.0% (1994) to 14.7% (2011) and from 10.0% (1994) to 7.4% (2011) in urban and rural sites respectively. Compared to the expected odds of prevalent HIV in this population, birth cohort effects peaked among pregnant women in the 1970-1974 birth cohort in urban sites (OR=1.41, 95% CI: 1.34, 1.49) and in the 1975-1979 birth cohort in rural sites (OR=1.29, 95% CI: 1.19, 1.38). Significantly lower odds of prevalent HIV infections were noted among pregnant women in birth cohort 1985-1989 (OR=0.79, 95% CI: 0.74, 0.84) and birth cohort 1990-1996 (OR= 0.68, 95% CI: 0.60, 0.76) in urban sites. Compared to 24 year-olds, age effects were elevated most for 26 year-olds (OR=1.13, 95% CI: 1.02, 1.25), and protective for 15 year-olds (OR=0.31, 95% CI: 0.29, 0.33) and 19 year-olds (OR=0.57, 95% CI: 0.56, 0.62).
The lower odds of prevalent HIV infections among younger generations (1985 to 1996 in urban sites may imply a possible fall in HIV incidence, and calls for preventive interventions that emphasize forestalling risky sexual behaviors among young people in order to suppress new HIV infections.
Advisors/Committee Members: Mary Lou Lindegren, MD , MPH (committee member), Marie Griffin, MD, MPH (committee member), Bryan Shepherd, PhD (committee member), Sten H. Vermund, MD, PhD (Committee Chair).
Subjects/Keywords: ANC; HIV; trends; Age-period-cohort
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APA (6th Edition):
Kasongo, W. (2013). Age-Period-Cohort and Educational Attainment Effects on HIV Prevalence in Zambian Pregnant Women, 1994 through 2011. (Doctoral Dissertation). Vanderbilt University. Retrieved from http://hdl.handle.net/1803/13829
Chicago Manual of Style (16th Edition):
Kasongo, Webster. “Age-Period-Cohort and Educational Attainment Effects on HIV Prevalence in Zambian Pregnant Women, 1994 through 2011.” 2013. Doctoral Dissertation, Vanderbilt University. Accessed March 08, 2021.
http://hdl.handle.net/1803/13829.
MLA Handbook (7th Edition):
Kasongo, Webster. “Age-Period-Cohort and Educational Attainment Effects on HIV Prevalence in Zambian Pregnant Women, 1994 through 2011.” 2013. Web. 08 Mar 2021.
Vancouver:
Kasongo W. Age-Period-Cohort and Educational Attainment Effects on HIV Prevalence in Zambian Pregnant Women, 1994 through 2011. [Internet] [Doctoral dissertation]. Vanderbilt University; 2013. [cited 2021 Mar 08].
Available from: http://hdl.handle.net/1803/13829.
Council of Science Editors:
Kasongo W. Age-Period-Cohort and Educational Attainment Effects on HIV Prevalence in Zambian Pregnant Women, 1994 through 2011. [Doctoral Dissertation]. Vanderbilt University; 2013. Available from: http://hdl.handle.net/1803/13829
Vanderbilt University
7.
Park, Sang Soo.
Inference on partially identified parameters: with applications to the evaluation of heterogeneous treatment effects.
Degree: PhD, Economics, 2008, Vanderbilt University
URL: http://hdl.handle.net/1803/12509
► My dissertation focuses on partial identification of the distribution function of treatment effects. When treatment effects are believed to be heterogeneous, policy evaluation often requires…
(more)
▼ My dissertation focuses on partial identification of the distribution function of treatment effects. When treatment effects are believed to be heterogeneous, policy evaluation often requires knowledge of the distribution function of potential treatment effects. This function cannot be point-identified unless information is available on the dependence structure between the potential outcomes with and without the treatment. At best, it can be partially identified by finding the upper and lower bounds.
This dissertation consists of four essays. The first essay develops a new approach to the inference on partially identified parameters. It presents new confidence intervals for partially identified parameters, which are asymptotically valid under plausible regularity assumptions. In later chapters, I use these confidence intervals to carry out statistical inference on the quantiles of treatment effects.
In the second essay, I explore partial identification of the distribution of treatment effects in the context of a randomized experiment. Nonparametric estimation and statistical inference on the upper and lower bounds for the distribution of treatment effects are proposed.
The third essay considers the partial identification and inference on the quantile function of treatment effects. In this essay, I apply the confidence intervals developed in the first essay and further develop them to obviate estimation of the marginal density functions.
In the fourth essay, I employ data from Project STAR, a randomized experiment designed to investigate the effects of class size reduction on students' performances. I propose a method of identifying the distribution of treatment effects conditional upon pre-treatment outcomes in order to be able to look into the heterogeneity more closely. Using this methodology, along with the theories and methods developed in the previous three essays, I find evidence that different subgroups have gained differently from class size reduction (i.e. heterogeneous treatment effects) and that the pattern of heterogeneity differs by ability level.
Advisors/Committee Members: Bryan Shepherd (committee member), Tong Li (committee member), James Foster (committee member), Kathryn Anderson (committee member), Yanqin Fan (Committee Chair).
Subjects/Keywords: heterogeneous treatment effects; partially identified parameters; Inference
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APA (6th Edition):
Park, S. S. (2008). Inference on partially identified parameters: with applications to the evaluation of heterogeneous treatment effects. (Doctoral Dissertation). Vanderbilt University. Retrieved from http://hdl.handle.net/1803/12509
Chicago Manual of Style (16th Edition):
Park, Sang Soo. “Inference on partially identified parameters: with applications to the evaluation of heterogeneous treatment effects.” 2008. Doctoral Dissertation, Vanderbilt University. Accessed March 08, 2021.
http://hdl.handle.net/1803/12509.
MLA Handbook (7th Edition):
Park, Sang Soo. “Inference on partially identified parameters: with applications to the evaluation of heterogeneous treatment effects.” 2008. Web. 08 Mar 2021.
Vancouver:
Park SS. Inference on partially identified parameters: with applications to the evaluation of heterogeneous treatment effects. [Internet] [Doctoral dissertation]. Vanderbilt University; 2008. [cited 2021 Mar 08].
Available from: http://hdl.handle.net/1803/12509.
Council of Science Editors:
Park SS. Inference on partially identified parameters: with applications to the evaluation of heterogeneous treatment effects. [Doctoral Dissertation]. Vanderbilt University; 2008. Available from: http://hdl.handle.net/1803/12509
Vanderbilt University
8.
Wu, Jisong.
Distributions of Treatment Effects in Switching Regimes Models: Partial Identification, Confidence Sets, and an Application.
Degree: PhD, Economics, 2009, Vanderbilt University
URL: http://hdl.handle.net/1803/12669
► This dissertation studies the distributions of treatment effects in switching regimes models (SRMs). First, we propose a general class of SRMs and provide simple estimators…
(more)
▼ This dissertation studies the distributions of treatment effects in switching regimes models (SRMs). First, we propose a general class of SRMs and provide simple estimators of average treatment effects. Second, we establish sharp bounds on the joint distribution of potential outcomes and the distribution of treatment effects in parametric and semi-parametric SRMs. Lastly, we apply our models to study the effect of accelerated underwriting on firm’s performance. The dissertation consists of four chapters. In Chapter One, we review the literature of treatment effect studies and selection models. In Chapter Two, we introduce a general class of SRMs via a copula approach. Specifically, we model the joint distribution of each outcome error and the selection error via Normal mean-variance mixture copulas. We extend Heckman's two-step estimation procedure to the new class of models. We include additional correction terms in the second step to account for skewness in the outcome errors. We construct simple estimators of average treatment effects and establish their asymptotic properties. Simulation results confirm the importance of accounting for skewness in the outcome errors. In Chapter Three, we establish sharp bounds on the joint distribution of potential outcomes and the distribution of treatment effects in parametric and semiparametric SRMs. Our results for parametric SRMs with normal mean-variance mixture errors extend some existing results for Gaussian SRMs and our results for semiparametric SRMs supplement the point identification results of Heckman (1990). Compared with the corresponding sharp bounds when selection is random, we observe that self selection tightens the bounds on the joint distribution of the potential outcomes and the distribution of treatment effects. In Chapter Four, we apply our econometric models to study the impact of accelerated underwriting of seasoned equity offerings (SEOs) on a firm's performance one year after the issuance. Our initial results indicate that the private information in a firm's choice of flotation methods has a significant positive impact on its operating performance measured by return on assets, and this significance can not be captured through conventional sample selection models.
Advisors/Committee Members: Bryan Shepherd (committee member), Ronald Masulis (committee member), Tong Li (committee member), Peter Rousseau (committee member), Yanqin Fan (Committee Chair).
Subjects/Keywords: switching regimes models; treatment effects
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APA (6th Edition):
Wu, J. (2009). Distributions of Treatment Effects in Switching Regimes Models: Partial Identification, Confidence Sets, and an Application. (Doctoral Dissertation). Vanderbilt University. Retrieved from http://hdl.handle.net/1803/12669
Chicago Manual of Style (16th Edition):
Wu, Jisong. “Distributions of Treatment Effects in Switching Regimes Models: Partial Identification, Confidence Sets, and an Application.” 2009. Doctoral Dissertation, Vanderbilt University. Accessed March 08, 2021.
http://hdl.handle.net/1803/12669.
MLA Handbook (7th Edition):
Wu, Jisong. “Distributions of Treatment Effects in Switching Regimes Models: Partial Identification, Confidence Sets, and an Application.” 2009. Web. 08 Mar 2021.
Vancouver:
Wu J. Distributions of Treatment Effects in Switching Regimes Models: Partial Identification, Confidence Sets, and an Application. [Internet] [Doctoral dissertation]. Vanderbilt University; 2009. [cited 2021 Mar 08].
Available from: http://hdl.handle.net/1803/12669.
Council of Science Editors:
Wu J. Distributions of Treatment Effects in Switching Regimes Models: Partial Identification, Confidence Sets, and an Application. [Doctoral Dissertation]. Vanderbilt University; 2009. Available from: http://hdl.handle.net/1803/12669
. 
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