Constructing a Coincident Index of Business Cycles without Assuming a One-factor Model
AbstractThe 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 InfoPaper provided by Singapore Management University, School of Economics in its series Working Papers with number 22-2004.
Length: 25 pages
Date of creation: Mar 2004
Date of revision: Oct 2004
Publication status: Published in SMU Economics and Statistics Working Paper Series
Other versions of this item:
- Yasutomo Murasawa & Roberto S. Mariano, 2004. "Constructing a Coincident Index of Business Cycles Without Assuming a One-Factor Model," Econometric Society 2004 Far Eastern Meetings 710, Econometric Society.
- C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models
- C43 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Index Numbers and Aggregation
- C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
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- 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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