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Unveiling Covariate Inclusion Structures In Economic Growth Regressions Using Latent Class Analysis

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.

Identiferoai:union.ndltd.org:VIENNA/oai:epub.wu-wien.ac.at:5674
Date January 2016
CreatorsCrespo Cuaresma, Jesus, Grün, Bettina, Hofmarcher, Paul, Humer, Stefan, Moser, Mathias
PublisherElsevier
Source SetsWirtschaftsuniversität Wien
LanguageEnglish
Detected LanguageEnglish
TypeArticle, PeerReviewed
Formatapplication/pdf
RightsCreative Commons: Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0)
Relationhttps://doi.org/10.1016/j.euroecorev.2015.03.009, http://www.elsevier.com, http://epub.wu.ac.at/5674/

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