Jointness In Bayesian Variable Selection With Applications To Growth Regression

The authors present a measure of jointness to explore dependence among regressors in the context of Bayesian model selection. The jointness measure they propose equals the posterior odds ratio between those models that include a set of variables and the models that only include proper subsets. They...

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Bibliographic Details
Main Author: Ley, Eduardo
Other Authors: Steel, Mark F. J.
Format: eBook
Language:English
Published: Washington, D.C The World Bank 2006
Subjects:
Online Access:
Collection: World Bank E-Library Archive - Collection details see MPG.ReNa
Description
Summary:The authors present a measure of jointness to explore dependence among regressors in the context of Bayesian model selection. The jointness measure they propose equals the posterior odds ratio between those models that include a set of variables and the models that only include proper subsets. They show its application in cross-country growth regressions using two data-sets from the model-averaging growth literature
Physical Description:17 p.