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Eigenvalue Ratio Estimators for the Number of Dynamic Factors

Author

Listed:
  • Maddalena Cavicchioli
  • Mario Forni
  • Marco Lippi
  • Paolo zaffaroni

Abstract

In this paper we introduce three dynamic eigenvalue ratio estimators for the number of dynamic factors. Two of them, the Dynamic Eigenvalue Ratio (DER) and the Dynamic Growth Ratio (DGR) are dynamic counterparts of the eigenvalue ratio estimators (ER and GR) proposed by Ahn and Horenstein (2013). The third, the Dynamic eigenvalue Difference Ratio (DDR), is a new one but closely related to the test statistic proposed by Onatsky (2009). The advantage of such estimators is that they do not require preliminary determination of discretionary parameters. Finally, a static counterpart of the latter estimator, called eigenvalue Difference Ratio estimator (DR), is also proposed. We prove consistency of such estimators and evaluate their performance under simulation. We conclude that both DDR and DR are valid alternatives to existing criteria. Application to real data gives new insights on the number of factors driving the US economy.

Suggested Citation

  • Maddalena Cavicchioli & Mario Forni & Marco Lippi & Paolo zaffaroni, 2016. "Eigenvalue Ratio Estimators for the Number of Dynamic Factors," Center for Economic Research (RECent) 123, University of Modena and Reggio E., Dept. of Economics "Marco Biagi".
    • Handle: RePEc:mod:recent:123
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    Cited by:

    1. Brownlees, Christian & Mesters, Geert, 2021. "Detecting granular time series in large panels," Journal of Econometrics, Elsevier, vol. 220(2), pages 544-561.

    More about this item

    Keywords

    Generalized dynamic factor model; dynamic principal components; number of factors; static factor model;
    All these keywords.

    JEL classification:

    • C01 - Mathematical and Quantitative Methods - - General - - - Econometrics
    • C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Estimation: General
    • C38 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Classification Methdos; Cluster Analysis; Principal Components; Factor Analysis

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