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

Crespo Cuaresma, J., Gruen, B., Hofmarcher, P., Humer, S., & Moser, M. (2016). Unveiling covariate inclusion structures in economic growth regressions using latent class analysis. European Economic Review 81 189-202. 10.1016/j.euroecorev.2015.03.009.

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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 pace formed by linear regression models reveals interesting patterns of complementarity and substitutabiliy across economic growth determinants.

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