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A new approach for estimating VAR systems in the mixed-frequency case

Author

Listed:
  • Lukas Koelbl

    (Accenture Digital, Accenture Austria)

  • Manfred Deistler

    (Vienna University of Technology)

Abstract

In this paper we present a new estimation procedure named MF-IVL for VAR systems in the case of mixed-frequency data, where the data maybe, e.g., stock or flow data. The main idea of this new procedure is to project the slow components on the present and past fast ones in order to create instrumental variables. This procedure is shown to be generically consistent. Our claim is that the procedure is fast and more accurate when compared to the extended Yule-Walker procedure. A comparison of these two procedures is given by simulation.

Suggested Citation

  • Lukas Koelbl & Manfred Deistler, 2020. "A new approach for estimating VAR systems in the mixed-frequency case," Statistical Papers, Springer, vol. 61(3), pages 1203-1212, June.
    • Handle: RePEc:spr:stpapr:v:61:y:2020:i:3:d:10.1007_s00362-018-0985-1
    DOI: 10.1007/s00362-018-0985-1
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    References listed on IDEAS

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    1. Eric Ghysels & Arthur Sinko & Rossen Valkanov, 2007. "MIDAS Regressions: Further Results and New Directions," Econometric Reviews, Taylor & Francis Journals, vol. 26(1), pages 53-90.
    2. Lukas Koelbl & Alexander Braumann & Elisabeth Felsenstein & Manfred Deistler, 2016. "Estimation of VAR Systems from Mixed-Frequency Data: The Stock and the Flow Case," Advances in Econometrics, in: Dynamic Factor Models, volume 35, pages 43-73, Emerald Group Publishing Limited.
    3. Anderson, Brian D.O. & Deistler, Manfred & Felsenstein, Elisabeth & Funovits, Bernd & Koelbl, Lukas & Zamani, Mohsen, 2016. "Multivariate Ar Systems And Mixed Frequency Data: G-Identifiability And Estimation," Econometric Theory, Cambridge University Press, vol. 32(4), pages 793-826, August.
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    Cited by:

    1. Alain Hecq & Marie Ternes & Ines Wilms, 2021. "Hierarchical Regularizers for Mixed-Frequency Vector Autoregressions," Papers 2102.11780, arXiv.org, revised Mar 2022.
    2. Alain Hecq & Marie Ternes & Ines Wilms, 2023. "Hierarchical Regularizers for Reverse Unrestricted Mixed Data Sampling Regressions," Papers 2301.10592, arXiv.org, revised Nov 2024.

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