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Factor models in high-dimensional time series—A time-domain approach

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  • Hallin, Marc
  • Lippi, Marco

Abstract

High-dimensional time series may well be the most common type of dataset in the so-called “big data” revolution, and have entered current practice in many areas, including meteorology, genomics, chemometrics, connectomics, complex physics simulations, biological and environmental research, finance and econometrics. The analysis of such datasets poses significant challenges, both from a statistical as well as from a numerical point of view. The most 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 methodological foundations of those models (dynamic factor models usually are described via a spectral approach), contrasting such concepts as commonality and idiosyncrasy, factors and common shocks, dynamic and static principal components. That time-domain approach emphasizes the fact that, contrary to the static factor models favored by practitioners, the so-called general dynamic factor model essentially does not impose any constraints on the data-generating process, but follows from a general representation result.

Suggested Citation

  • Hallin, Marc & Lippi, Marco, 2013. "Factor models in high-dimensional time series—A time-domain approach," Stochastic Processes and their Applications, Elsevier, vol. 123(7), pages 2678-2695.
  • Handle: RePEc:eee:spapps:v:123:y:2013:i:7:p:2678-2695
    DOI: 10.1016/j.spa.2013.04.001
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    References listed on IDEAS

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    Citations

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    Cited by:

    1. Marc Hallin & Siegfried Hörmann & Marco Lippi, 2018. "Optimal dimension reduction for high-dimensional and functional time series," Statistical Inference for Stochastic Processes, Springer, vol. 21(2), pages 385-398, July.
    2. 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.
    3. Barigozzi, Matteo & Cho, Haeran & Fryzlewicz, Piotr, 2018. "Simultaneous multiple change-point and factor analysis for high-dimensional time series," Journal of Econometrics, Elsevier, vol. 206(1), pages 187-225.
    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. Matteo Barigozzi & Marc Hallin, 2016. "Generalized dynamic factor models and volatilities: recovering the market volatility shocks," Econometrics Journal, Royal Economic Society, vol. 19(1), pages 33-60, February.
    6. 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.
    7. Trucíos, Carlos & Mazzeu, João H. G. & Hallin, Marc & Hotta, Luiz K. & Pereira, Pedro L. Valls & Zevallos, Mauricio, 2019. "Forecasting conditional covariance matrices in high-dimensional time series: a general dynamic factor approach," Textos para discussão 505, FGV EESP - Escola de Economia de São Paulo, Fundação Getulio Vargas (Brazil).
    8. repec:taf:jnlasa:v:111:y:2016:i:515:p:1121-1131 is not listed on IDEAS
    9. repec:taf:quantf:v:17:y:2017:i:8:p:1205-1221 is not listed on IDEAS

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