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Dynamic Factor Analysis with Nonlinear Temporal Aggregation Constraints

Listed author(s):
  • Tommaso Proietti

    (Dipartimento di Scienze Statistiche, Università di Udine)

  • Filippo Moauro

    (ISTAT, Rome)

The paper estimates an index of coincident economic indicators for the U.S. economy using time series with different frequencies of observation (monthly and quarterly, possibly with missing values). The model considered is the dynamic factor model proposed by Stock and Watson, specified in the logarithms of the original variables and at the monthly frequency, which poses a problem of temporal aggregation with a nonlinear observational constraint when quarterly time series are included. Our main methodological contribution is to provide an exact solution to this problem, that hinges on conditional mode estimation by extended Kalman filtering and smoothing. On the empirical side the contribution of the paper is to provide monthly estimates of quarterly indicators, among which Gross Domestic Product, that are consistent with the quarterly totals.

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Paper provided by EconWPA in its series Econometrics with number 0401003.

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Length: 20 pages
Date of creation: 07 Jan 2004
Handle: RePEc:wpa:wuwpem:0401003
Note: Type of Document - ; prepared on WinXP; pages: 20; figures: 2
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References listed on IDEAS
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  1. Roberto S. Mariano & Yasutomo Murasawa, 2003. "A new coincident index of business cycles based on monthly and quarterly series," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 18(4), pages 427-443.
  2. Siem Jan Koopman & Neil Shephard & Jurgen A. Doornik, 1999. "Statistical algorithms for models in state space using SsfPack 2.2," Econometrics Journal, Royal Economic Society, vol. 2(1), pages 107-160.
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