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Factor Extraction in Dynamic Factor Models: Kalman Filter Versus Principal Components

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  • Esther Ruiz
  • Pilar Poncela

Abstract

This survey looks at the literature on factor extraction in the context of Dynamic Factor Models (DFMs) fitted to multivariate systems of economic and financial variables. Many of the most popular factor extraction procedures often used in empirical applications are based on either Principal Components (PC) or Kalman filter and smoothing (KFS) techniques. First, we show that the KFS factors are a weighted average of the contemporaneous information (PC factors) and the past information and that the weights of the latter are negligible unless the factors are close to the non-stationarity boundary and/or their loadings are pretty small when compared with the variance-covariance matrix of the idiosyncratic components. Note that the weight of the past can be large either because the cross-sectional dimension is small or because the magnitude of the factor loadings is small. Consequently, we are able to explain why, in practice, there is a general consensus about PC and KFS factors being rather similar when extracted from stationary systems of large dimensions. Second, we survey how PC and KFS deal with several issues often faced in the context of extracting factors from real data systems. In particular, we describe PC and KFS procedures to deal with mixed frequencies and missing observations, structural breaks, nonstationarity, Markov-switching parameters or multi-level factor structures. In general, we see that KFS is very flexible to deal with these issues.

Suggested Citation

  • Esther Ruiz & Pilar Poncela, 2022. "Factor Extraction in Dynamic Factor Models: Kalman Filter Versus Principal Components," Foundations and Trends(R) in Econometrics, now publishers, vol. 12(2), pages 121-231, November.
    • Handle: RePEc:now:fnteco:0800000039
    DOI: 10.1561/0800000039
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    References listed on IDEAS

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    Cited by:

    1. Matteo Barigozzi, 2023. "Asymptotic equivalence of Principal Components and Quasi Maximum Likelihood estimators in Large Approximate Factor Models," Papers 2307.09864, arXiv.org, revised Jun 2024.
    2. Juan, Aranzazu de & Poncela, Maria Pilar, 2023. "Economic activity and C02 emissions in Spain," DES - Working Papers. Statistics and Econometrics. WS 37975, Universidad Carlos III de Madrid. Departamento de Estadística.
    3. Jad Beyhum & Jonas Striaukas, 2023. "Factor-augmented sparse MIDAS regressions with an application to nowcasting," Papers 2306.13362, arXiv.org, revised Nov 2024.
    4. Philipp Gersing & Christoph Rust & Manfred Deistler, 2023. "Weak Factors are Everywhere," Papers 2307.10067, arXiv.org, revised Jan 2024.
    5. Fresoli, Diego & Poncela, Pilar & Ruiz, Esther, 2023. "Ignoring cross-correlated idiosyncratic components when extracting factors in dynamic factor models," Economics Letters, Elsevier, vol. 230(C).
    6. Matteo Barigozzi, 2023. "Quasi Maximum Likelihood Estimation of High-Dimensional Factor Models: A Critical Review," Papers 2303.11777, arXiv.org, revised May 2024.
    7. 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.
    8. Diego Fresoli & Pilar Poncela & Esther Ruiz, 2024. "Dealing with idiosyncratic cross-correlation when constructing confidence regions for PC factors," Papers 2407.06883, arXiv.org.

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