Further Results on Bayesian Method of Moments Analysis of the Multiple Regression Model
In this article we extend previous BMOM results by showing how information about a variance parameter and its relation to regression coefficients produces a rich class of postdata densities for regression parameters. Prediction and model selection techniques are also described. We also discuss the well-documented link between cross-entropy and the average log odds and then use this criterion in an experiment to compare results obtained from BMOM and Bayes approaches using data generated from known models.
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 2001|
|Publication status:||Published in International Economic Review 2001, vol. 42, pp. 121-139|
|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:12021. 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.