IDEAS home Printed from https://ideas.repec.org/a/jns/jbstat/v241y2021i1p71-117n1.html
   My bibliography  Save this article

Determining the Number of Factors in Static Approximate Factor Models Using Discrete Fourier Transforms and Pseudo-Eigenvalues

Author

Listed:
  • Ruan Weihua

    (Department of Mathematics, Statistics, and Computer Science, Purdue University Northwest, 2200 169th St., Hammond, IN 46323-2094, USA)

  • Hou Qian

    (School of Mathematics and Science, Shanghai Normal University, Shanghai, Shanghai, China)

Abstract

This paper is concerned with the applications of discrete Fourier transforms in identification of the number of common factors of static approximate factor models. We report and explain the effects of discrete Fourier transforms to matrices, and show how the effects can be used to improve the performance of a number of eigenvalue-based estimators. In addition, we develop a set of pseudo-eigenvalues using cross-sectional discrete Fourier transforms, and use them to develop new estimators. Mathematical proofs of the consistency of the new estimators are provided, and Monte Carlo experiments are conducted to compare the new estimators with some existing ones in the literature.

Suggested Citation

  • Ruan Weihua & Hou Qian, 2021. "Determining the Number of Factors in Static Approximate Factor Models Using Discrete Fourier Transforms and Pseudo-Eigenvalues," Journal of Economics and Statistics (Jahrbuecher fuer Nationaloekonomie und Statistik), De Gruyter, vol. 241(1), pages 71-117, February.
    • Handle: RePEc:jns:jbstat:v:241:y:2021:i:1:p:71-117:n:1
    DOI: 10.1515/jbnst-2019-0056
    as

    Download full text from publisher

    File URL: https://doi.org/10.1515/jbnst-2019-0056
    Download Restriction: no
    File URL: https://libkey.io/10.1515/jbnst-2019-0056?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    More about this item

    Keywords

    discrete Fourier transforms; common factors; idiosyncratic errors; pseudo-eigenvalues; static approximate factor models;
    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

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:jns:jbstat:v:241:y:2021:i:1:p:71-117:n:1. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Peter Golla (email available below). General contact details of provider: https://www.degruyter.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.