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This dissertation applies the Bayesian approach as a method to improve the estimation efficiency of existing econometric tools. The first chapter suggests the Continuous Choice Bayesian (CCB) estimator which combines the Bayesian approach with the Continuous Choice (CC) estimator suggested by Imai and Keane (2004). Using simulation study, I provide

This dissertation applies the Bayesian approach as a method to improve the estimation efficiency of existing econometric tools. The first chapter suggests the Continuous Choice Bayesian (CCB) estimator which combines the Bayesian approach with the Continuous Choice (CC) estimator suggested by Imai and Keane (2004). Using simulation study, I provide two important findings. First, the CC estimator clearly has better finite sample properties compared to a frequently used Discrete Choice (DC) estimator. Second, the CCB estimator has better estimation efficiency when data size is relatively small and it still retains the advantage of the CC estimator over the DC estimator. The second chapter estimates baseball's managerial efficiency using a stochastic frontier function with the Bayesian approach. When I apply a stochastic frontier model to baseball panel data, the difficult part is that dataset often has a small number of periods, which result in large estimation variance. To overcome this problem, I apply the Bayesian approach to a stochastic frontier analysis. I compare the confidence interval of efficiencies from the Bayesian estimator with the classical frequentist confidence interval. Simulation results show that when I use the Bayesian approach, I achieve smaller estimation variance while I do not lose any reliability in a point estimation. Then, I apply the Bayesian stochastic frontier analysis to answer some interesting questions in baseball.
ContributorsChoi, Kwang-shin (Author) / Ahn, Seung (Thesis advisor) / Mehra, Rajnish (Committee member) / Park, Sungho (Committee member) / Arizona State University (Publisher)
Created2014
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This paper comes from a consulting project that the consulting firm, New Venture Group (NVG), did for a hospital in the southwest United States. The name of the hospital as well as the names of the hospitalists and units for the hospital will be withheld for confidentiality reasons. The hospital

This paper comes from a consulting project that the consulting firm, New Venture Group (NVG), did for a hospital in the southwest United States. The name of the hospital as well as the names of the hospitalists and units for the hospital will be withheld for confidentiality reasons. The hospital will be referred to as the ‘client’ throughout this paper. New Venture Group is a management consulting firm associated with Arizona State University (ASU), W.P. Carey School of Business and The Barrett Honors College. NVG recruits their consultants directly from the upper-class student body. NVG takes on projects from a wide variety of clients to provide real-world solutions comparable to that of other management consulting firms in the industry.
The client wanted to look into ways to improve patient satisfaction. To improve patient satisfaction the consulting team performed research and held a data collection. The team researched literature for possible improvements in technology, management procedures, and hospital operations protocols. The team then provided the findings and possible implementations to the client. Another item the team looked into was communication between night shift hospitalists and nurses, and possible ways to improve their communication. In the winter of 2010 a data collection was held at the client hospital that measured several different metrics of hospitalist
urse communication. In early 2011 a NVG team provided a descriptive statistics analysis of the results to the client. After the team’s first presentation I joined NVG and the team with this client. The client wanted to dig deeper into the data to find any patterns that were inherent in the data that were not immediately obvious from descriptive statistics. To do this I built over a 150 different regressions to dig from the data as many different patterns that could be found. Most of these regressions found many non-interesting results and a few did find significant interesting results. A report was sent to the client with all the results found. This paper is structured differently than the one delivered to the client in that only the significant interesting results are included and terminology will used for an audience who is familiar with statistics and mathematics. The work in this paper is the combined result of the whole team. My most specific input in this project is the quantitative analysis section. The other parts of this paper are also included so that the reader can see the full results of this consulting project.
ContributorsGuggisberg, Michael (Author) / Ahn, Seung (Thesis director) / Brooks, Daniel (Committee member) / Werner, Kathleen (Contributor) / Barrett, The Honors College (Contributor)
Created2012-05