Growth regressions, principal components and frequentist model averaging

Abstract

This paper offers two innovations for empirical growth research. First, the paper discusses principal components augmented regressions to take into account all available information in well-behaved regressions. Second, the paper proposes a frequentist model averaging framework as an alternative to Bayesian model averaging approaches. The proposed methodology is applied to three data sets, including the Sala-i-Martin et al. (2004) and Fernandez et al. (2001) data as well as a data set of the European Union member states' regions. Key economic variables are found to be significantly related to economic growth. The findings highlight the relevance of the proposed methodology for empirical economic growth research.