Improving the Composite Indices of Business Cycles by Minimum Distance Factor Analysis

Abstract

Minimum distance factor analysis (MD-FA) is applicable to stationary ergodic sequences. The estimated common factors, or factor scores, are weighted averages of the current observable variables, i.e., more informative variables receive larger weights. MD-FA of the U.S. coincident business cycle indicators (BCI) gives a new U.S. coincident composite index (CI) of business cycles. The weights on the BCIs for the new CI are similar to those for the Stock-Watson Experimental Coincident Index (XCI).