Publication: Jointness in Bayesian Variable Selection with Applications to Growth Regression
Steel, Mark F. J.
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.
“Ley, Eduardo; Steel, Mark F. J.. 2006. Jointness in Bayesian Variable Selection with Applications to Growth Regression. Policy Research Working Paper; No. 4063. © http://openknowledge.worldbank.org/entities/publication/87dcc363-8772-5a4c-a6e3-6381d914d38f License: CC BY 3.0 IGO.”
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