On the Predictive Distributions of Outcome Gains in the Presence of an Unidentified Parameter

D.J. Poirier and Justin Tobias ()

Working Papers from California Irvine - School of Social Sciences

Abstract: 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 the entire distributions associated with these gains can be accurately obtained on certain situations.

Keywords: DISTRIBUTION; OUTCOME; INFORMATION (search for similar items in EconPapers)
JEL-codes: C11 C15 C51 (search for similar items in EconPapers)
Pages: 37 pages
Date: 2001
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Related works:
Journal Article: On the Predictive Distributions of Outcome Gains in the Presence of an Unidentified Parameter (2003)
Working Paper: On the Predictive Distributions of Outcome Gains in the Presence of an Unidentified Parameter (2003)
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Persistent link: https://EconPapers.repec.org/RePEc:fth:calirv:00-01-30

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