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Activity Number:
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38
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Type:
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Topic Contributed
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Date/Time:
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Sunday, August 11, 2002 : 4:00 PM to 5:50 PM
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Sponsor:
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Section on Bayesian Stat. Sciences*
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| Abstract - #300752 |
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Title:
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Estimating the Predictive Distributions of Outcome Gains in the Presence of an Unidentified Parameter
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Author(s):
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Dale Poirier*+ and Justin Tobias
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Affiliation(s):
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University of California, Irvine and University of California, Irvine
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Address:
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3151 Social Sciences Plaza, Irvine, California, 92697-5100, USA
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Keywords:
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Bayesian ; potential outcome ; switching regression
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Abstract:
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In this paper we describe methods for obtaining the predictive distributions of outcome gains in the framework of a standard latent variable selection model. While most previous work has focused on estimation of mean treatment parameters as the method for characterizing outcome gains from program participation, we show how the entire distributions associated with these gains can be obtained. Although the out-of-sample outcome gain distributions depend on an unidentified parameter, we use the results of Koop and Poirier (1997) to show that learning can take place about this parameter through information contained in the identified parameters via a positive definiteness restriction on the covariance matrix. In cases where this type of learning is not highly informative, the spread of the predictive distributions depends more critically on the prior. We show both theoretically and in extensive generated data experiments how learning takes place, and delineate the sensitivity of our results to the prior specifications.
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