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. DOI:10.1016/j.euroecorev.2015.03.009.

[img] Text
Unveiling covariate inclusion structures in economic growth regressions using latent class analysis.pdf - Accepted Version
Restricted to Repository staff only until January 2020.
Available under License Creative Commons Attribution Non-commercial No Derivatives.

Download (655kB)

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.

Item Type: Article
Uncontrolled Keywords: Economic Growth Determinants; Bayesian Model Averaging; Latent Class Analysis; Dirichlet Processes
Research Programs: World Population (POP)
Bibliographic Reference: European Economic Review; 81:189-202 [January 2016] (Published online 3 April 2015)
Depositing User: IIASA Import
Date Deposited: 15 Jan 2016 08:54
Last Modified: 28 Mar 2017 10:14
URI: http://pure.iiasa.ac.at/11694

Actions (login required)

View Item View Item

International Institute for Applied Systems Analysis (IIASA)
Schlossplatz 1, A-2361 Laxenburg, Austria
Phone: (+43 2236) 807 0 Fax:(+43 2236) 71 313