Sequential testing for structural stability in approximate factor models

Matteo Barigozzi and Lorenzo Trapani

Discussion Papers from University of Nottingham, Granger Centre for Time Series Econometrics

Abstract: We develop an on-line monitoring procedure to detect a change in a large approximate factor model. Our statistics are based on a well-known property of the (r + 1)-th eigenvalue of the sample covariance matrix of the data (having defined r as the number of common factors): whilst under the null the (r + 1)-th eigenvalue is bounded, under the alternative of a change (either in the loadings, or in the number of factors itself) it becomes spiked. Given that the sample eigenvalue cannot be estimated consistently under the null, we regularise the problem by randomising the test statistic in conjunction with sample conditioning, obtaining a sequence of i.i.d., asymptotically chi-square statistics which are then employed to build the monitoring scheme. Numerical evidence shows that our procedure works very well in finite samples, with a very small probability of false detections and tight detection times in presence of a genuine change-point.

Keywords: large factor model; change-point; sequential testing; randomised tests. (search for similar items in EconPapers)
Date: 2018-04
New Economics Papers: this item is included in nep-ets
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.nottingham.ac.uk/research/groups/grangercentre/documents/18-04.pdf (application/pdf)

Related works:
Journal Article: Sequential testing for structural stability in approximate factor models (2020)
Working Paper: Sequential testing for structural stability in approximate factor models (2020)
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:not:notgts:18/04

Access Statistics for this paper

More papers in Discussion Papers from University of Nottingham, Granger Centre for Time Series Econometrics School of Economics University of Nottingham University Park Nottingham NG7 2RD. Contact information at EDIRC.
Bibliographic data for series maintained by ().