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来源类型Article
规范类型其他
DOI10.1016/j.euroecorev.2015.03.009
Unveiling covariate inclusion structures in economic growth regressions using latent class analysis.
Crespo Cuaresma J; Gruen B; Hofmarcher P; Humer S; Moser M
发表日期2016
出处European Economic Review 81: 189-202
出版年2016
语种英语
摘要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.
主题World Population (POP)
关键词Economic Growth Determinants Bayesian Model Averaging Latent Class Analysis Dirichlet Processes
URLhttp://pure.iiasa.ac.at/id/eprint/11694/
来源智库International Institute for Applied Systems Analysis (Austria)
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资源类型智库出版物
条目标识符http://119.78.100.153/handle/2XGU8XDN/130728
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GB/T 7714
Crespo Cuaresma J,Gruen B,Hofmarcher P,et al. Unveiling covariate inclusion structures in economic growth regressions using latent class analysis.. 2016.
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