On the Predictive Distributions of Outcome Gains in the Presence of an Unidentified Parameter
In this article we describe methods for obtaining the predictive distributions of outcome gains in the framework of a standard latent variable selection model. Although 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 in certain situations. Although the out-of sample outcome gain distributions depend on an unidentified parameter, we use the results of Koop and Poirier 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.
To our knowledge, this item is not available for
download. To find whether it is available, there are three
1. Check below under "Related research" whether another version of this item is available online.
2. Check on the provider's web page whether it is in fact available.
3. Perform a search for a similarly titled item that would be available.
|Date of creation:||01 Jan 2003|
|Date of revision:|
|Publication status:||Published in Journal of Business & Economic Statistics 2003, vol. 21, pp. 258-268|
|Contact details of provider:|| Postal: Iowa State University, Dept. of Economics, 260 Heady Hall, Ames, IA 50011-1070|
Phone: +1 515.294.6741
Fax: +1 515.294.0221
Web page: http://www.econ.iastate.edu
More information through EDIRC
When requesting a correction, please mention this item's handle: RePEc:isu:genres:12014. See general information about how to correct material in RePEc.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Curtis Balmer)
If references are entirely missing, you can add them using this form.