Bi, Daning; Shang, Han Lin; Yang, Yanrong; Zhu, Huanjun - 2021
This paper proposes a new AR-sieve bootstrap approach on high-dimensional time series. The major challenge of classical bootstrap methods on high-dimensional time series is two-fold: the curse dimensionality and temporal dependence. To tackle such difficulty, we utilise factor modelling to...