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Dynamic Factor Analysis in The Presence of Missing Data

Author

Listed:
  • B. Jungbacker

    (VU University Amsterdam)

  • S.J. Koopman

    (VU University Amsterdam)

  • M. van der Wel

    (Erasmus University Rotterdam, and CREATES)

Abstract

This paper concerns estimating parameters in a high-dimensional dynamic factormodel by the method of maximum likelihood. To accommodate missing data in theanalysis, we propose a new model representation for the dynamic factor model. Itallows the Kalman filter and related smoothing methods to evaluate the likelihoodfunction and to produce optimal factor estimates in a computationally efficient waywhen missing data is present. The implementation details of our methods for signalextraction and maximum likelihood estimation are discussed. The computational gainsof the new devices are presented based on simulated data sets with varying numbersof missing entries.

Suggested Citation

  • B. Jungbacker & S.J. Koopman & M. van der Wel, 2009. "Dynamic Factor Analysis in The Presence of Missing Data," Tinbergen Institute Discussion Papers 09-010/4, Tinbergen Institute, revised 11 Mar 2011.
    • Handle: RePEc:tin:wpaper:20090010
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    References listed on IDEAS

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    7. Forni, Mario & Lippi, Marco, 2001. "The Generalized Dynamic Factor Model: Representation Theory," Econometric Theory, Cambridge University Press, vol. 17(6), pages 1113-1141, December.
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    9. Marta Bańbura & Michele Modugno, 2014. "Maximum Likelihood Estimation Of Factor Models On Datasets With Arbitrary Pattern Of Missing Data," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 29(1), pages 133-160, January.
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    Cited by:

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    2. Grassi, Stefano & Proietti, Tommaso & Frale, Cecilia & Marcellino, Massimiliano & Mazzi, Gianluigi, 2015. "EuroMInd-C: A disaggregate monthly indicator of economic activity for the Euro area and member countries," International Journal of Forecasting, Elsevier, vol. 31(3), pages 712-738.

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    More about this item

    Keywords

    High-dimensional vector series; Kalman filtering and smooting; Maximum likelihood; Unbalanced panels of time series;
    All these keywords.

    JEL classification:

    • C33 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Models with Panel Data; Spatio-temporal Models
    • C43 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Index Numbers and Aggregation

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