A Reduced Rank Regression Approach to Coincident and Leading Indexes Building
AbstractThis 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. Copyright Blackwell Publishing Ltd and the Department of Economics, University of Oxford 2007.
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Bibliographic InfoArticle provided by Department of Economics, University of Oxford in its journal Oxford Bulletin of Economics and Statistics.
Volume (Year): 69 (2007)
Issue (Month): 2 (04)
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Other versions of this item:
- Cubadda, Gianluca, 2004. "A Reduced Rank Regression Approach to Coincident and Leading Indexes Building," Economics & Statistics Discussion Papers esdp04022, University of Molise, Dept. EGSeI.
- C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
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