Sequential testing for structural stability in approximate factor models

Matteo Barigozzi and Lorenzo Trapani

Stochastic Processes and their Applications, 2020, vol. 130, issue 8, 5149-5187

Abstract: We develop a monitoring procedure to detect changes in a large approximate factor model. Letting r be the number of common factors, we base our statistics on the fact that the r+1-th eigenvalue of the sample covariance matrix is bounded under the null of no change, whereas it becomes spiked under changes. Given that sample eigenvalues cannot be estimated consistently under the null, we randomise the test statistic, obtaining a sequence of i.i.d. statistics, which are used for the monitoring scheme. Numerical evidence shows a very small probability of false detections, and tight detection times of change-points.

Keywords: Large factor model; Change-point; Sequential testing; Randomised tests (search for similar items in EconPapers)
Date: 2020
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Citations: View citations in EconPapers (2)

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Related works:
Working Paper: Sequential testing for structural stability in approximate factor models (2020)
Working Paper: Sequential testing for structural stability in approximate factor models (2018)
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DOI: 10.1016/j.spa.2020.03.003

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