A Comprehensive Approach to Posterior Jointness Analysis in Bayesian Model Averaging Applications
Jesus Crespo Cuaresma (),
Bettina Grün (),
Paul Hofmarcher (),
Stefan Humer () and
Mathias Moser ()
Department of Economics Working Paper Series from WU Vienna University of Economics and Business
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. (authors' abstract)
Keywords: Bayesian Model Averaging; Jointness; Robust Growth Determinants; Machine Learning; Association Rules; Bayes-Verfahren; Wirtschaftswachstum; robuste Statistik (search for similar items in EconPapers)
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Working Paper: A Comprehensive Approach to Posterior Jointness Analysis in Bayesian Model Averaging Applications (2015)
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Persistent link: https://EconPapers.repec.org/RePEc:wiw:wus005:4493
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