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.
Mariano, Roberto S. and Murasawa, Yasutomo.
Constructing a Coincident Index of Business Cycles without Assuming a One-Factor Model. (2004). Research Collection School Of Economics.
Available at: http://ink.library.smu.edu.sg/soe_research/795
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