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Application of Bayesian Methods to Structural Models and Stochastic Frontier Production Models


Abstract 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... (more)
Created Date 2014
Contributor Choi, Kwang-shin (Author) / Ahn, Seung (Advisor) / Mehra, Rajnish (Committee member) / Park, Sungho (Committee member) / Arizona State University (Publisher)
Subject Economics / Bayesian approach / Stochastic Frontier Analysis / Structural Estimation
Type Doctoral Dissertation
Extent 90 pages
Language English
Copyright
Reuse Permissions All Rights Reserved
Note Doctoral Dissertation Economics 2014
Collaborating Institutions Graduate College / ASU Library
Additional Formats MODS / OAI Dublin Core / RIS


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Description Dissertation/Thesis