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Constructing a Coincident Index of Business Cycles without Assuming a One-factor Model

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  • ROBERTO S. MARIANO

    ()
    (School of Economics and Social Sciences, Singapore Management University)

  • YASUTOMO MURASAWA

    ()
    (University of Pennsylvania)

Abstract

The Stock–Watson coincident index and its subsequent extensions assume a static linear one-factor structure for the component indicators. Such assumption is restrictive in practice, however, with as few as four indicators. In fact, such assumption is unnecessary if one defines a coincident index as an estimate of latent monthly real GDP. This paper considers VAR and factor models for latent monthly real GDP and other coincident indicators, and estimates the models using the observable mixed-frequency series. For US data, Schwartz’s Bayesian information criterion selects a two-factor model. The smoothed estimate of latent monthly real GDP is the proposed index.

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Bibliographic Info

Paper provided by Singapore Management University, School of Economics in its series Working Papers with number 22-2004.

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Length: 25 pages
Date of creation: Mar 2004
Date of revision: Oct 2004
Publication status: Published in SMU Economics and Statistics Working Paper Series
Handle: RePEc:siu:wpaper:22-2004

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Cited by:
  1. Paul Viefers, 2011. "Bayesian Inference for the Mixed-Frequency VAR Model," Discussion Papers of DIW Berlin 1172, DIW Berlin, German Institute for Economic Research.

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