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On the robustness of the general dynamic factor model with infinite-dimensional space: identification, estimation, and forecasting

Author

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  • Carlos Cesar Trucios-Maza
  • João H. G Mazzeu
  • Luis K. Hotta
  • Pedro L. Valls Pereira
  • Marc Hallin

Abstract

General dynamic factor models have demonstrated their capacity to circumvent the curse of dimensionality in time series and have been successfully applied in many economic and financial applications. However, their performance in the presence of outliers has not been analysed yet. In this paper, we study the impact of additive outliers on the identification, estimation and forecasting performance of general dynamic factor models. Based on our findings, we propose robust identification, estimation and forecasting procedures. Our proposal is evaluated via Monte Carlo experiments and in empirical data.

Suggested Citation

  • Carlos Cesar Trucios-Maza & João H. G Mazzeu & Luis K. Hotta & Pedro L. Valls Pereira & Marc Hallin, 2019. "On the robustness of the general dynamic factor model with infinite-dimensional space: identification, estimation, and forecasting," Working Papers ECARES 2019-32, ULB -- Universite Libre de Bruxelles.
    • Handle: RePEc:eca:wpaper:2013/298201
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    Keywords

    Dimension reduction; Forecast; Jumps; Large panels;
    All these keywords.

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