11694 10 32096 5 50958 document 32096 Unveiling covariate inclusion structures in economic growth regressions using latent class analysis.pdf application/pdf e248a77e556156e3ce7aeca1eddf5027 MD5 655332 2016-01-25 10:50:58 https://pure.iiasa.ac.at/id/eprint/11694/1/Unveiling%20covariate%20inclusion%20structures%20in%20economic%20growth%20regressions%20using%20latent%20class%20analysis.pdf 11694 1 1 application/pdf text en public cc_by_nc_nd

Unveiling covariate inclusion structures in economic growth regressions using latent class analysis.pdf

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http://eprints.org/relation/isVersionOf https://pure.iiasa.ac.at/id/document/32096 http://eprints.org/relation/isVolatileVersionOf https://pure.iiasa.ac.at/id/document/32096 http://eprints.org/relation/isIndexCodesVersionOf https://pure.iiasa.ac.at/id/document/32096 archive 351 disk0/00/01/16/94 2016-01-15 08:54:22 2021-08-27 17:25:34 2016-01-15 08:54:22 article show Crespo Cuaresma J. 1838 Gruen B. Hofmarcher P. Humer S. Moser M. pub iis_ecn prog_pop Economic Growth Determinants; Bayesian Model Averaging; Latent Class Analysis; Dirichlet Processes 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 pace formed by linear regression models reveals interesting patterns of complementarity and substitutabiliy across economic growth determinants. 2016-01 published Elsevier 10.1016/j.euroecorev.2015.03.009 XJ-16-034 European Economic Review; 81:189-202 [January 2016] (Published online 3 April 2015) [doi:10.1016/j.euroecorev.2015.03.009] 58 public European Economic Review 81 189-202 TRUE 1873-572X FALSE no info:eu-repo/semantics/article