Dynamic Factor Analysis with Nonlinear Temporal Aggregation Constraints
AbstractThe 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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Bibliographic InfoPaper provided by EconWPA in its series Econometrics with number 0401003.
Length: 20 pages
Date of creation: 07 Jan 2004
Date of revision:
Note: Type of Document - ; prepared on WinXP; pages: 20; figures: 2
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Other versions of this item:
- Tommaso Proietti & Filippo Moauro, 2006. "Dynamic factor analysis with non-linear temporal aggregation constraints," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 55(2), pages 281-300.
- C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
This paper has been announced in the following NEP Reports:
- NEP-ALL-2004-01-12 (All new papers)
- NEP-ECM-2004-01-25 (Econometrics)
- NEP-ETS-2004-01-12 (Econometric Time Series)
Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.:
- Neil Shephard & Jurgen Doornik & Siem Jan Koopman, 1998.
"Statistical algorithms for models in state space using SsfPack 2.2,"
Economics Series Working Papers
1998-W06, University of Oxford, Department of Economics.
- 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.
- Koopman, S.J.M. & Shephard, N. & Doornik, J.A., 1998. "Statistical Algorithms for Models in State Space Using SsfPack 2.2," Discussion Paper 1998-141, Tilburg University, Center for Economic Research.
- 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.
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