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

Crespo Cuaresma, Jesus ORCID: https://orcid.org/0000-0003-3244-6560 and Grün, Bettina and Hofmarcher, Paul and Humer, Stefan and Moser, Mathias (2015) A Comprehensive Approach to Posterior Jointness Analysis in Bayesian Model Averaging Applications. Department of Economics Working Paper Series, 193. WU Vienna University of Economics and Business, Vienna.


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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.

Item Type: Paper
Keywords: Bayesian Model Averaging / Jointness / Robust Growth Determinants / Machine Learning / Association Rules / Bayes-Verfahren / Wirtschaftswachstum / robuste Statistik
Classification Codes: RVK QH 233; JEL C11, C55, O40
Divisions: Departments > Volkswirtschaft
Depositing User: Claudia Tering-Raunig
Date Deposited: 19 Mar 2015 10:57
Last Modified: 15 Oct 2021 14:46
URI: https://epub.wu.ac.at/id/eprint/4493


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