BRIC and MINT: Statistical Learning of Expert Opinion on Emerging Markets

• Activity Number: 43
• Type: Contributed
• Date/Time: Sunday, August 9, 2015 : 2:00 PM to 3:50 PM
• Sponsor: Section on Statistical Learning and Data Mining
• Author(s): Eftychia Solea* and Bing Li and Aleksandra B. Slavkovic
• Companies: and Penn State and Penn State
• Keywords: economics ; BRIC ; dimension reduction ; kernel methods ; PCA ; kernel PCA

Abstract

BRIC is an acronym that refers to four economies: Brazil, Russia, India and China which are considered to be the new advanced emerging economies. The acronym has been widely used by economists emphasizing the shift of global economy toward the developing world. In this paper, we propose dimension reduction techniques to the problem of identifying new emerging economies. In particular, we have investigated economic data sets from 2000 to 2012 using dimension reduction techniques: PCA, Kernel PCA and Generalized SIR focusing on the following: to highlight patterns and trends for economies and to identify new emerging economies.

We identify about 11 countries that might be able to emerge that represent a broad range of areas such as Pakistan, Bangladesh, Iran, Saudi Arabia, Egypt, Poland, Argentina, Philippines, South Africa, Thailand and Colombia.

Our results give insight into trends and patterns of economic performance, and our analysis can provide a fertile ground for future researches on economic studies related to emerging markets.