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A robust procedure to build dynamic factor models with cluster structure

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  • Alonso, Andrés M.
  • Galeano, Pedro
  • Peña, Daniel

Abstract

Dynamic factor models provide a useful way to model large sets of time series. These data often have heterogeneity and cluster structure and the formulation and estimation of dynamic factor models should be adapted to these features. This article presents a procedure to fit Dynamic Factor Models with Cluster Structure (DFMCS), where some of the factors are global and others group-specific, to heterogeneous data that may include multivariate additive outliers and level shifts. The procedure starts with an initial cleaning of the times series from outlying effects. Then a first estimation of the possible factors is applied to the cleaned data and these factors are used to build the common component of each series. The groups are found by studying the joint dependency of these common components. Then, additional factors are estimated by using the series in each cluster and, finally, all the factors found are classified as global or group-specific. We show in a Monte Carlo study that the procedure works well and seems to be better than other alternatives in terms of estimation of factors and loadings as well as in terms of misclassification rates for the series. An example of an electricity market is presented to illustrate the advantages of cleaning for outliers and taking into account the cluster structure for understanding and forecasting.

Suggested Citation

  • Alonso, Andrés M. & Galeano, Pedro & Peña, Daniel, 2020. "A robust procedure to build dynamic factor models with cluster structure," Journal of Econometrics, Elsevier, vol. 216(1), pages 35-52.
    • Handle: RePEc:eee:econom:v:216:y:2020:i:1:p:35-52
    DOI: 10.1016/j.jeconom.2020.01.004
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    Cited by:

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    3. Matteo Barigozzi & Marc Hallin, 2023. "Dynamic Factor Models: a Genealogy," Papers 2310.17278, arXiv.org, revised Jan 2024.
    4. Camacho, Maximo & Lopez-Buenache, German, 2023. "Factor models for large and incomplete data sets with unknown group structure," International Journal of Forecasting, Elsevier, vol. 39(3), pages 1205-1220.
    5. Blasques, Francisco & Hoogerkamp, Meindert Heres & Koopman, Siem Jan & van de Werve, Ilka, 2021. "Dynamic factor models with clustered loadings: Forecasting education flows using unemployment data," International Journal of Forecasting, Elsevier, vol. 37(4), pages 1426-1441.
    6. Camacho, Maximo, 2020. "What do international energy prices have in common after taking into account the key drivers?," DES - Working Papers. Statistics and Econometrics. WS 31647, Universidad Carlos III de Madrid. Departamento de Estadística.
    7. Boudt, Kris & Heyndels, Ewoud, 2024. "Robust interactive fixed effects," Econometrics and Statistics, Elsevier, vol. 29(C), pages 206-223.
    8. Massimiliano Caporin & C. Vladimir Rodríguez-Caballero & Esther Ruiz, 2024. "The factor structure of exchange rates volatility: global and intermittent factors," Empirical Economics, Springer, vol. 67(1), pages 31-45, July.
    9. Daniel Peña & Ruey S. Tsay, 2023. "A testing approach to clustering scalar time series," Journal of Time Series Analysis, Wiley Blackwell, vol. 44(5-6), pages 667-685, September.
    10. Zhe Sun & Yundong Tu, 2024. "Factors in Fashion: Factor Analysis towards the Mode," Papers 2409.19287, arXiv.org.
    11. Camacho, Maximo & Caro, Angela & Peña, Daniel, 2023. "What drives industrial energy prices?," Economic Modelling, Elsevier, vol. 120(C).

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    More about this item

    Keywords

    Clustering time series; Dependency measures; Multivariate additive outliers; Multivariate level shifts; Principal components;
    All these keywords.

    JEL classification:

    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models
    • C51 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Construction and Estimation

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