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Abstract Details

Activity Number: 656
Type: Contributed
Date/Time: Thursday, August 2, 2012 : 10:30 AM to 12:20 PM
Sponsor: Section on Statistics in Marketing
Abstract - #305695
Title: Bayesian Analysis on Unobserved Components
Author(s): Woosuk Kim*+
Companies:
Address: 8849 Harperpoint Dr, Cincinnati, OH, 45249, United States
Keywords: MCMC ; Discrete choice models ; Bayesian
Abstract:

Different discrete choice models are derived under various specifications of the density of unobserved factor. We develop a discrete choice model under the assumption that the unobserved factors follow the multivariate skew normal distribution. Under the assumptions, we compute the choice probability of each category in the model. Because it is difficult to get a closed-form choice probability, we consider the Bayesian estimation using MCMC. Moreover, under the model assumptions, we explore whether or not the model can handle random taste variation, allows any pattern of substitution, and is applicable to panel data with temporally correlated errors.


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