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Dynamic Factor Models in gretl. The DFM package

Author

Listed:
  • Riccardo (Jack) Lucchetti

    (DISES, Facoltà di Economia "Giorgio Fuà ")

  • Ioannis A. Venetis

    (Department of Economics, University of Patras)

Abstract

This package deals with the estimation of dynamic factor models (DFM); for the moment, three factor extraction techniques are available, but we plan to add more in future versions. Further additions will include parameter restrictions.

Suggested Citation

  • Riccardo (Jack) Lucchetti & Ioannis A. Venetis, 2019. "Dynamic Factor Models in gretl. The DFM package," gretl working papers 7, Universita' Politecnica delle Marche (I), Dipartimento di Scienze Economiche e Sociali.
    • Handle: RePEc:anc:wgretl:7
    as

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    File URL: http://docs.dises.univpm.it/web/quaderni/pdfgretl/gretl007.pdf
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    References listed on IDEAS

    as
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    16. Marta Bańbura & Michele Modugno, 2014. "Maximum Likelihood Estimation Of Factor Models On Datasets With Arbitrary Pattern Of Missing Data," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 29(1), pages 133-160, January.
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    19. Jushan Bai & Peng Wang, 2015. "Identification and Bayesian Estimation of Dynamic Factor Models," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 33(2), pages 221-240, April.
    Full references (including those not matched with items on IDEAS)

    Citations

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    Cited by:

    1. Lucchetti, Riccardo & Venetis, Ioannis A., 2020. "A replication of "A quasi-maximum likelihood approach for large, approximate dynamic factor models" (Review of Economics and Statistics, 2012)," Economics - The Open-Access, Open-Assessment E-Journal (2007-2020), Kiel Institute for the World Economy (IfW Kiel), vol. 14, pages 1-14.
    2. Salamaliki, Paraskevi, 2019. "Assessing labor market conditions in Greece: a note," MPRA Paper 97559, University Library of Munich, Germany.

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    More about this item

    Keywords

    Dynamic factor models; EM algorithm; Kalman filter; 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
    • C38 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Classification Methdos; Cluster Analysis; Principal Components; Factor Analysis
    • C87 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - Econometric Software

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