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High-Dimensional Dynamic Factor Models: A Selective Survey and Lines of Future Research

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  • Lippi, Marco
  • Deistler, Manfred
  • Anderson, Brian

Abstract

High-Dimensional Dynamic Factor Models are presented in detail: The main assumptions and their motivation, main results, illustrations by means of elementary examples. In particular, the role of singular ARMA models in the theory and applications of High-Dimensional Dynamic Factor Models is discussed. The emphasis is on model classes and their structure theory, rather than on estimation in the narrow sense. The survey is not comprehensive. Its aim is to point out promising lines of research and applications that have not yet been sufficiently developed.

Suggested Citation

  • Lippi, Marco & Deistler, Manfred & Anderson, Brian, 2023. "High-Dimensional Dynamic Factor Models: A Selective Survey and Lines of Future Research," Econometrics and Statistics, Elsevier, vol. 26(C), pages 3-16.
    • Handle: RePEc:eee:ecosta:v:26:y:2023:i:c:p:3-16
    DOI: 10.1016/j.ecosta.2022.03.008
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    References listed on IDEAS

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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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    More about this item

    Keywords

    High-dimensional vector processes; Dynamic factor models; State-space representations; Singular ARMA vector processes;
    All these keywords.

    JEL classification:

    • C50 - Mathematical and Quantitative Methods - - Econometric Modeling - - - General
    • C55 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Large Data Sets: Modeling and Analysis
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods

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