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A Reduced Rank Regression Approach to Coincident and Leading Indexes Building *
Author(s) -
Cubadda Gianluca
Publication year - 2007
Publication title -
oxford bulletin of economics and statistics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.131
H-Index - 73
eISSN - 1468-0084
pISSN - 0305-9049
DOI - 10.1111/j.1468-0084.2006.00196.x
Subject(s) - rank (graph theory) , index (typography) , econometrics , regression , rank correlation , composite index , linear regression , mathematics , statistics , business cycle , regression analysis , composite indicator , polynomial , correlation , computer science , economics , combinatorics , keynesian economics , mathematical analysis , world wide web , geometry
This paper proposes a reduced rank regression framework for constructing a coincident index (CI) and a leading index (LI). Based on a formal definition that requires that the first differences of the LI are the best linear predictor of the first differences of the CI, it is shown that the notion of polynomial serial correlation common features can be used to build these composite variables. Concepts and methods are illustrated by an empirical investigation of the US business cycle indicators.