4 papers
Tail-robust estimation of factor-adjusted vector autoregressive models for high-dimensional time series
Dylan Dijk, Haeran Cho
We study the problem of modelling high-dimensional, heavy-tailed time series data via a factor-adjusted vector autoregressive (VAR) model, which simultaneously accounts for pervasi…
Detection and Mode-Identification of Multiple Change Points in Tensor Factor Models
Yuqi Zhang, Zetai Cen, Haeran Cho
We study the problems arising from modeling high-dimensional tensor-valued time series under a Tucker decomposition-based factor model with multiple structural change points. First…
Tail-robust factor modelling of vector and tensor time series in high dimensions
Matteo Barigozzi, Haeran Cho, Hyeyoung Maeng
We study the problem of factor modelling vector- and tensor-valued time series in the presence of heavy tails in the data, which produce extreme observations with non-negligible pr…
Moving sum procedure for multiple change point detection in large factor models
Matteo Barigozzi, Haeran Cho, Lorenzo Trapani
This paper proposes a moving sum methodology for detecting multiple change points in high-dimensional time series under a factor model, where changes are attributed to those in loa…