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

Crespo Cuaresma, Jesus ORCID: https://orcid.org/0000-0003-3244-6560 and Grün, Bettina and Hofmarcher, Paul and Humer, Stefan and Moser, Mathias (2016) Unveiling Covariate Inclusion Structures In Economic Growth Regressions Using Latent Class Analysis. European Economic Review, 81. pp. 189-202. ISSN 0014-2921

Available under License Creative Commons: Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0).

Download (631kB)


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.

Item Type: Article
Additional Information: To see the final version of this paper please visit the publisher's website. Access to the published version may require a subscription. The original publication is available at www.elsevier.com.
Keywords: Economic growth determinants; Bayesian model averaging; Latent class analysis; Dirichlet processes
Classification Codes: JEL C11; C21; O47
Divisions: Departments > Volkswirtschaft > Makroökonomie
Forschungsinstitute > Human Capital and Development
Forschungsinstitute > Verteilungsfragen
Kompetenzzentren > Sustainability Transf. & Responsibility
Version of the Document: Accepted for Publication
Depositing User: Gertraud Novotny
Date Deposited: 07 Aug 2017 09:33
Last Modified: 02 Dec 2019 09:21
Related URLs:
FIDES Link: https://bach.wu.ac.at/d/research/results/71188/
URI: https://epub.wu.ac.at/id/eprint/5674


View Item View Item


Downloads per month over past year

View more statistics