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Factor Models in High-Dimensional Time Series: A Time-Domain Approach

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  • Marc Hallin
  • Marco Lippi

Abstract

High-dimensional time series may well be the most common type of dataset in the socalled“big data” revolution, and have entered current practice in many areas, includingmeteorology, genomics, chemometrics, connectomics, complex physics simulations, biologicaland environmental research, finance and econometrics. The analysis of such datasetsposes significant challenges, both from a statistical as from a numerical point of view. Themost successful procedures so far have been based on dimension reduction techniques and,more particularly, on high-dimensional factor models. Those models have been developed,essentially, within time series econometrics, and deserve being better known in other areas.In this paper, we provide an original time-domain presentation of the methodologicalfoundations of those models (dynamic factor models usually are described via a spectralapproach), contrasting such concepts as commonality and idiosyncrasy, factors and commonshocks, dynamic and static principal components. That time-domain approach emphasizesthe fact that, contrary to the static factor models favored by practitioners, the so-called generaldynamic factor model essentially does not impose any constraints on the data-generatingprocess, but follows from a general representation result.

Suggested Citation

  • Marc Hallin & Marco Lippi, 2013. "Factor Models in High-Dimensional Time Series: A Time-Domain Approach," Working Papers ECARES ECARES 2013-15, ULB -- Universite Libre de Bruxelles.
  • Handle: RePEc:eca:wpaper:2013/142428
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    References listed on IDEAS

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    Cited by:

    1. Marc Hallin & Siegfried Hörmann & Marco Lippi, 2017. "Optimal Dimension Reduction for High-dimensional and Functional Time Series," Working Papers ECARES ECARES 2017-39, ULB -- Universite Libre de Bruxelles.
    2. Matteo Barigozzi & Marc Hallin, 2015. "Networks, Dynamic Factors, and the Volatility Analysis of High-Dimensional Financial Series," Working Papers ECARES ECARES 2015-34, ULB -- Universite Libre de Bruxelles.
    3. Forni, Mario & Hallin, Marc & Lippi, Marco & Zaffaroni, Paolo, 2017. "Dynamic factor models with infinite-dimensional factor space: Asymptotic analysis," Journal of Econometrics, Elsevier, vol. 199(1), pages 74-92.
    4. F. Della Marra, 2017. "A forecasting performance comparison of dynamic factor models based on static and dynamic methods," Economics Department Working Papers 2017-ME01, Department of Economics, Parma University (Italy).
    5. repec:taf:jnlasa:v:111:y:2016:i:515:p:1121-1131 is not listed on IDEAS
    6. repec:spr:sistpr:v:21:y:2018:i:2:d:10.1007_s11203-018-9172-1 is not listed on IDEAS

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