Statistical learning on emerging economies

Abstract

ABSTRACTBRIC is an acronym coined by Jim O'Neill from Goldman Sachs in 2001 to abbreviate four emerging economies, Brazil, Russia, India and China, based on economic data at the time. Later, as new data became available, Goldman Sachs updated this list to include Mexico, Indonesia, Nigeria and Turkey, which was referred to as MINT. This list, as well as some other similar lists of emerging economies, is based on descriptive statistics of the economic data combined with economists' insights. The purpose of this study is twofold: to see if these insights into the global economic trends can be learned with statistical learning tools, and, if so, to identify the next emerging countries. We apply both unsupervised and supervised learning methods, which include linear and nonlinear principle component analysis, and nonlinear sufficient dimension reduction, to 13 years worth of economic data. Our results show that these statistical learning techniques, and in particular the kernel sliced inverse regression algorithm, can serve as a useful tool for economists and policy-makers for analyzing global economic trends, by its ability to incorporate large amount of economic data and previous experts' judgments, which otherwise may take years of experiences to acquire.