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Statistical learning on emerging economies

机译：新兴经济体的统计学习

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摘要

BRIC 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.
机译：金砖四国（BRIC）是高盛（Goldman Sachs）2001年的吉姆·奥尼尔（Jim O'Neill）的首字母缩写，根据当时的经济数据缩写了四个新兴经济体，即巴西，俄罗斯，印度和中国。后来，随着新数据的获得，高盛（Goldman Sachs）更新了该列表，以包括墨西哥，印度尼西亚，尼日利亚和土耳其，这被称为MINT。该列表以及一些其他类似的新兴经济体列表，是基于对经济数据的描述性统计以及经济学家的见解而得出的。这项研究的目的是双重的：查看是否可以使用统计学习工具来了解这些对全球经济趋势的见解；如果可以，则确定下一个新兴国家。我们将无监督和有监督的学习方法（包括线性和非线性主成分分析以及非线性充分降维）应用于13年的经济数据。我们的结果表明，这些统计学习技术，尤其是核仁切片的逆回归算法，可以利用其整合大量经济数据和先前专家的能力，成为经济学家和决策者分析全球经济趋势的有用工具。的判断，否则可能需要多年的经验才能掌握。

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