University of Leicester
Browse
Identification and estimation of a large factor model with structural instability.pdf (309.31 kB)

Identification and estimation of a large factor model with structural instability

Download (309.31 kB)
journal contribution
posted on 2017-03-10, 10:04 authored by Badi H. Baltagi, Chihwa Kao, Fa Wang
This paper tackles the identification and estimation of a high dimensional factor model with unknown number of latent factors and a single break in the number of factors and/or factor loadings occurring at unknown common date. First, we propose a least squares estimator of the change point based on the second moments of estimated pseudo factors and show that the estimation error of the proposed estimator is Op(1). We also show that the proposed estimator has some degree of robustness to misspecification of the number of pseudo factors. With the estimated change point plugged in, consistency of the estimated number of pre and post-break factors and convergence rate of the estimated pre and post-break factor space are then established under fairly general assumptions. The finite sample performance of our estimators is investigated using Monte Carlo experiments.

History

Citation

Journal of Econometrics, 2017, 197 (1), pp. 87-100

Author affiliation

/Organisation/COLLEGE OF SOCIAL SCIENCES, ARTS AND HUMANITIES/Department of Economics

Version

  • AM (Accepted Manuscript)

Published in

Journal of Econometrics

Publisher

Elsevier

issn

0304-4076

Acceptance date

2016-10-27

Copyright date

2016

Available date

2018-11-12

Publisher version

http://www.sciencedirect.com/science/article/pii/S0304407616302032

Notes

The file associated with this record is under embargo until 24 months after publication, in accordance with the publisher's self-archiving policy. The full text may be available through the publisher links provided above.

Language

en

Usage metrics

    University of Leicester Publications

    Categories

    Exports

    RefWorks
    BibTeX
    Ref. manager
    Endnote
    DataCite
    NLM
    DC