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Linear System Challenges of Dynamic Factor Models

Author

Listed:
  • Brian D. O. Anderson

    (School of Engineering, Australian National University, Acton, Canberra, ACT 2601, Australia)

  • Manfred Deistler

    (Research Unit of Econometrics and System Theory, Technische Universität Wien, Wiednerhauptstrasse 8, 1040 Vienna, Austria
    Department of Mathematics and Statistics, Wirtschaftsuniversität Wien, Welthandelsplatz 1, 1020 Vienna, Austria)

  • Marco Lippi

    (Einaudi Institute for Economics and Finance, Via Sallustiana, 62, 00187 Rome, Italy)

Abstract

A survey is provided dealing with the formulation of modelling problems for dynamic factor models, and the various algorithm possibilities for solving these modelling problems. Emphasis is placed on understanding requirements for the handling of errors, noting the relevance of the proposed application of the model, be it for example prediction or business cycle determination. Mixed frequency problems are also considered, in which certain entries of an underlying vector process are only available for measurement at a submultiple frequency of the original process. Certain classes of processes are shown to be generically identifiable, and others not to have this property.

Suggested Citation

  • Brian D. O. Anderson & Manfred Deistler & Marco Lippi, 2022. "Linear System Challenges of Dynamic Factor Models," Econometrics, MDPI, vol. 10(4), pages 1-26, December.
    • Handle: RePEc:gam:jecnmx:v:10:y:2022:i:4:p:35-:d:995168
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    References listed on IDEAS

    as
    1. Alexei Onatski, 2009. "Testing Hypotheses About the Number of Factors in Large Factor Models," Econometrica, Econometric Society, vol. 77(5), pages 1447-1479, September.
    2. Mario Forni & Marc Hallin & Marco Lippi & Lucrezia Reichlin, 2000. "The Generalized Dynamic-Factor Model: Identification And Estimation," The Review of Economics and Statistics, MIT Press, vol. 82(4), pages 540-554, November.
    3. Bai, Jushan & Ng, Serena, 2007. "Determining the Number of Primitive Shocks in Factor Models," Journal of Business & Economic Statistics, American Statistical Association, vol. 25, pages 52-60, January.
    4. Stock, J.H. & Watson, M.W., 2016. "Dynamic Factor Models, Factor-Augmented Vector Autoregressions, and Structural Vector Autoregressions in Macroeconomics," Handbook of Macroeconomics, in: J. B. Taylor & Harald Uhlig (ed.), Handbook of Macroeconomics, edition 1, volume 2, chapter 0, pages 415-525, Elsevier.
    5. Sims, Christopher A, 1980. "Macroeconomics and Reality," Econometrica, Econometric Society, vol. 48(1), pages 1-48, January.
    6. Amengual, Dante & Watson, Mark W., 2007. "Consistent Estimation of the Number of Dynamic Factors in a Large N and T Panel," Journal of Business & Economic Statistics, American Statistical Association, vol. 25, pages 91-96, January.
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    Cited by:

    1. Tamás Szabados, 2023. "Factorization of a Spectral Density with Smooth Eigenvalues of a Multidimensional Stationary Time Series," Econometrics, MDPI, vol. 11(2), pages 1-11, May.

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