A Comprehensive Approach to Posterior Jointness Analysis in Bayesian Model Averaging Applications

Jesus Crespo Cuaresma, Bettina Grün, Paul Hofmarcher, Stefan Humer, Mathias Moser

Publication: Working/Discussion PaperWU Working Paper

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Abstract

Posterior analysis in Bayesian model averaging (BMA) applications often includes the assessment of measures of jointness (joint inclusion) across covariates.
We link the discussion of jointness measures in the econometric literature to the literature on association rules in data mining exercises. We analyze a group of alternative jointness measures that include those proposed in the BMA literature and several others put forward in the field of data mining. The way these measures address the joint exclusion of covariates appears particularly important in terms of the conclusions that can be drawn from them. Using a dataset of economic growth determinants, we assess how the measurement of jointness in BMA can affect inference about the structure of bivariate inclusion patterns across covariates.
Original languageEnglish
Place of PublicationVienna
PublisherWU Vienna University of Economics and Business
Publication statusPublished - 1 Mar 2015

Publication series

NameDepartment of Economics Working Paper Series
No.193

WU Working Paper Series

  • Department of Economics Working Paper Series

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