Advanced Search
MyIDEAS: Login

A New Method for Determining the Number of Factors in Factor Models with Large Datasets

Contents:

Author Info

  • George Kapetanios

    ()
    (Queen Mary, University of London)

Registered author(s):

    Abstract

    The paradigm of a factor model is very appealing and has been used extensively in economic analyses. Underlying the factor model is the idea that a large number of economic variables can be adequately modelled by a small number of indicator variables. Throughout this extensive research activity on large dimensional factor models a major preoccupation has been the development of tools for determining the number of factors needed for modelling. This paper provides an alternative method to information criteria as tools for estimating the number of factors in large dimensional factor models. The theoretical properties of the method are explored and an extensive Monte Carlo study is undertaken. Results are favourable for the new method and suggest that it is a reasonable alternative to existing methods.

    Download Info

    If you experience problems downloading a file, check if you have the proper application to view it first. In case of further problems read the IDEAS help page. Note that these files are not on the IDEAS site. Please be patient as the files may be large.
    File URL: http://www.econ.qmul.ac.uk/papers/doc/wp525.pdf
    Download Restriction: no

    Bibliographic Info

    Paper provided by Queen Mary, University of London, School of Economics and Finance in its series Working Papers with number 525.

    as in new window
    Length:
    Date of creation: Oct 2004
    Date of revision:
    Handle: RePEc:qmw:qmwecw:wp525

    Contact details of provider:
    Postal: London E1 4NS
    Phone: +44 (0) 20 7882 5096
    Fax: +44 (0) 20 8983 3580
    Web page: http://www.econ.qmul.ac.uk
    More information through EDIRC

    Related research

    Keywords: Factor models; Large sample covariance matrix; Maximum eigenvalue;

    Find related papers by JEL classification:

    This paper has been announced in the following NEP Reports:

    References

    References listed on IDEAS
    Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.:
    as in new window
    1. Filippo Altissimo & Valentina Corradi, 2000. "Strong Rules for Detecting the Number of Breaks in a Time Series," Econometric Society World Congress 2000 Contributed Papers 0574, Econometric Society.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as in new window

    Cited by:
    1. Ghate, Chetan & Wright, Stephen, 2012. "The “V-factor”: Distribution, timing and correlates of the great Indian growth turnaround," Journal of Development Economics, Elsevier, vol. 99(1), pages 58-67.
    2. Alexander Chudik & M. Hashem Pesaran, 2013. "Large panel data models with cross-sectional dependence: a survey," Globalization and Monetary Policy Institute Working Paper 153, Federal Reserve Bank of Dallas.
    3. GUO-FITOUSSI, Liang, 2013. "A Comparison of the Finite Sample Properties of Selection Rules of Factor Numbers in Large Datasets," MPRA Paper 50005, University Library of Munich, Germany.

    Lists

    This item is not listed on Wikipedia, on a reading list or among the top items on IDEAS.

    Statistics

    Access and download statistics

    Corrections

    When requesting a correction, please mention this item's handle: RePEc:qmw:qmwecw:wp525. See general information about how to correct material in RePEc.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Nick Vriend).

    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.

    If references are entirely missing, you can add them using this form.

    If the full references list an item that is present in RePEc, but the system did not link to it, you can help with 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 profile, as there may be some citations waiting for confirmation.

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