Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
5066553 | European Economic Review | 2016 | 14 Pages |
Abstract
We propose the use of Latent Class Analysis methods to analyze the covariate inclusion patterns across specifications resulting from Bayesian model averaging exercises. Using Dirichlet Process clustering, we are able to identify and describe dependency structures among variables in terms of inclusion in the specifications that compose the model space. We apply the method to two datasets of potential determinants of economic growth. Clustering the posterior covariate inclusion structure of the model space formed by linear regression models reveals interesting patterns of complementarity and substitutability across economic growth determinants.
Related Topics
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Economics and Econometrics
Authors
Jesus Crespo Cuaresma, Bettina Grün, Paul Hofmarcher, Stefan Humer, Mathias Moser,