The ratio of Indian to US per capita output over the past 45 years has displayed a distinctive V-shaped pattern. We show that a strikingly similar V-shaped pattern is visible not just in aggregate output .figures, but also as the primary determinant of long-term movements in the cross-sectional...

Following Bai (2004) and Bai and Ng (2004) we estimate a common factor representation of a panel of output series for India, disaggregated by 15 states and 14 broad industry groups. We find that a single common V-Factor accounts for a large part of the significant shift in the cross-sectional...

Principal component analysis (PCA) is a widely used dimension reduction tool in the analysis of high-dimensional data. However, in many applications such as risk quantification in finance or climatology, one is interested in capturing the tail variations rather than variation around the mean. In...

Principal component analysis (PCA) is a widely used dimension reduction tool in the analysis of many kind of high-dimensional data. It is used in signal processing, mechanical engineering, psychometrics, and other fields under different names. It still bears the same mathematical idea: the...