Detection of Multiple Structural Breaks in Large Covariance Matrix

Thursday 17 May 2018, 1.00PM to 2.00pm

Speaker(s): Degui Li

Abstract: This paper studies multiple structural breaks in large covariance matrix of high-dimensional time series variables satisfying the approximate factor model structure. The breaks in the second-order structure of the common components are due to sudden changes in either factor loadings or covariance of latent factors, requiring appropriate transformation of the factor models to facilitate estimation of the (transformed) common factors and factor loadings via the classical principal component analysis. With the estimated factors and idiosyncratic errors, an easy-to-implement CUSUM-based detection technique is introduced to consistently estimate the location and number of breaks and correctly identify whether they originate in the common or idiosyncratic error components. The algorithms of wild binary segmentation and sparsified binary segmentation are used to estimate the breaks in the common and idiosyncratic error components, respectively. Under some mild conditions, the asymptotic properties of the proposed method are derived with near-optimal rates (up to a logarithmic factor) achieved for the estimated change points. Extensive simulation studies show that the finite-sample performance of our method is superior to that introduced by Barigozzi et al (2018) in most settings.

Location: A/EC202 Economics Staff Room

Admission: Staff and PhD Students