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Pooling information and forecasting with dynamic factor analysis

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  • Peña, Daniel
  • Poncela, Pilar

Abstract

In this paper, we present a generalized dynamic factor model for a vector of time series, which seems to provide a general framework to incorporate all the common information included in a collection of variables. The common dynamic structure is explained through a set of common factors, which may be stationary, or nonstationary as in the case of common trends. Also, it may exist a specific structure for each variable. Identification of the non stationary factors is made through the common eigenstructure of the lagged co variance matrices. Estimation of the model is carried out in state space form with the EM algorithm, where the Kalman filter is used to estimate the factors or not observable variables. It is shown that this approach implies, as particular cases, many pooled forecasting procedures suggested in the literature. In particular, it offers an explanation to the empirical fact that the forecasting performance of a time series vector is improved when the overall mean is incorporated into the forecast equation for each component.

Suggested Citation

  • Peña, Daniel & Poncela, Pilar, 1996. "Pooling information and forecasting with dynamic factor analysis," DES - Working Papers. Statistics and Econometrics. WS 10709, Universidad Carlos III de Madrid. Departamento de Estadística.
  • Handle: RePEc:cte:wsrepe:10709
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    References listed on IDEAS

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    1. Hannan, E J, 1976. "The Identification and Parameterization of ARMAX and State Space Forms," Econometrica, Econometric Society, vol. 44(4), pages 713-723, July.
    2. Alvaro Escribano & Daniel Peña, 1994. "Cointegration And Common Factors," Journal of Time Series Analysis, Wiley Blackwell, vol. 15(6), pages 577-586, November.
    3. Min, Chung-ki & Zellner, Arnold, 1993. "Bayesian and non-Bayesian methods for combining models and forecasts with applications to forecasting international growth rates," Journal of Econometrics, Elsevier, vol. 56(1-2), pages 89-118, March.
    4. Clemen, Robert T., 1989. "Combining forecasts: A review and annotated bibliography," International Journal of Forecasting, Elsevier, vol. 5(4), pages 559-583.
    5. Zellner, Arnold & Hong, Chansik, 1989. "Forecasting international growth rates using Bayesian shrinkage and other procedures," Journal of Econometrics, Elsevier, vol. 40(1), pages 183-202, January.
    6. Gonzalo, Jesus & Granger, Clive W J, 1995. "Estimation of Common Long-Memory Components in Cointegrated Systems," Journal of Business & Economic Statistics, American Statistical Association, vol. 13(1), pages 27-35, January.
    7. R. H. Shumway & D. S. Stoffer, 1982. "An Approach To Time Series Smoothing And Forecasting Using The Em Algorithm," Journal of Time Series Analysis, Wiley Blackwell, vol. 3(4), pages 253-264, July.
    8. Watson, Mark W. & Engle, Robert F., 1983. "Alternative algorithms for the estimation of dynamic factor, mimic and varying coefficient regression models," Journal of Econometrics, Elsevier, vol. 23(3), pages 385-400, December.
    9. Hannan, E J, 1971. "The Identification Problem for Multiple Equation Systems with Moving Average Errors," Econometrica, Econometric Society, vol. 39(5), pages 751-765, September.
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    Keywords

    Co integration and common factors;

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