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Dynamic Factor Models

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Macroeconomic Forecasting in the Era of Big Data

Part of the book series: Advanced Studies in Theoretical and Applied Econometrics ((ASTA,volume 52))

Abstract

Dynamic factor models are parsimonious representations of relationships among time series variables. With the surge in data availability, they have proven to be indispensable in macroeconomic forecasting. This chapter surveys the evolution of these models from their pre-big-data origins to the large-scale models of recent years. We review the associated estimation theory, forecasting approaches, and several extensions of the basic framework.

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Notes

  1. 1.

    When (f t) and (e t) are not Gaussian, the procedure gives QML estimators.

  2. 2.

    A weaker assumption can also be used: \(0< \underline {c} \leq \liminf \limits _{n \to \infty }\lambda _r(\frac {\boldsymbol \Lambda '\boldsymbol \Lambda }{n})<\limsup \limits _{n \to \infty } \lambda _1(\frac {\boldsymbol \Lambda '\boldsymbol \Lambda }{n})\leq \overline {c}< \infty \) (see Doz, Giannone, & Reichlin, 2011).

  3. 3.

    Mixed data sampling (MIDAS) regression models, proposed by Ghysels, Santa-Clara, and Valkanov (2004), represent an alternative way of dealing with missing data.

  4. 4.

    The working paper version appeared in 1994 in NBER Working Papers 4643.

  5. 5.

    Without this approximation, the Kalman filter would be untractable, since it would be necessary to take the M T possible trajectories of S 1, …, S T. For further details, see Kim (1994) and the references therein.

  6. 6.

    Their model combines the MS-MIDAS model (Guérin & Marcellino, 2013) and the factor-MIDAS model (Marcellino & Schumacher, 2010).

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  1. Paris School of Economics and University Paris 1 Panthéon-Sorbonne, Paris, France

    Catherine Doz

  2. UHERO and Department of Economics, University of Hawaii, Honolulu, HI, USA

    Peter Fuleky

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Doz, C., Fuleky, P. (2020). Dynamic Factor Models. In: Fuleky, P. (eds) Macroeconomic Forecasting in the Era of Big Data. Advanced Studies in Theoretical and Applied Econometrics, vol 52. Springer, Cham. https://doi.org/10.1007/978-3-030-31150-6_2

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