A Reduced Rank Regression Approach to Coincident and Leading Indexes Building

Gianluca Cubadda

Economics & Statistics Discussion Papers from University of Molise, Department of Economics

Abstract: This paper proposes a reduced rank regression framework for constructing coincident and leading indexes. Based on a formal definition that requires that the first differences of the leading index are the best linear predictor of the first differences of the coincident index, 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.

Keywords: Coincident and Leading Indexes; Polynomial Serial Correlation Common Feature; Reduced Rank Regression. (search for similar items in EconPapers)
JEL-codes: C32 (search for similar items in EconPapers)
Pages: 28 pages
Date: 2004-09-24
New Economics Papers: this item is included in nep-bec and nep-ecm
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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Journal Article: A Reduced Rank Regression Approach to Coincident and Leading Indexes Building* (2007)
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Persistent link: https://EconPapers.repec.org/RePEc:mol:ecsdps:esdp04022

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