Kin Wai Keith Chan (CUHK)
DateWednesday November 17, 2021
11:00 am - 12:00 pm
LocationJeffery Hall 225 & Online via Zoom
Wednesday, November 17th, 2021
Time: 11:00 a.m. Place: Online via Zoom (contact Brian Ling for Zoom link)
Speaker: Kin Wai Keith Chan (CUHK)
Title: A general and optimal difference-based method for variance estimation in time series
Abstract: Difference-based statistics are asymptotically invariant to arbitrary mean structures. Thus, they are natural building blocks for constructing variance estimators. In this talk, we present a general framework for constructing variance estimators based on observations that are masked by serial dependence structures and time-varying mean structures. The proposed class of estimators is general enough to cover many existing estimators. Necessary and sufficient conditions for consistency are investigated. The first asymptotically optimal estimator is derived. Our proposed estimator is theoretically proven to be invariant to arbitrary mean structures, which may include trends and a possibly divergent number of discontinuities.
Short bio: Kin Wai Chan is an Assistant Professor in the Department of Statistics at the Chinese University of Hong Kong. He completed his B.Sc. and M.Phil. in Risk Management Science in 2013 and 2015, respectively. After that, he did graduate work in Statistics from Harvard University under the supervision of Xiao-Li Meng and received his Ph.D. in 2018. His research interest is statistical inference for dependent data and incomplete data. Many of his research articles are about long-run variance estimation, change point analysis, and multiple imputation. He is particularly keen on developing elegant statistical theories and creating new methodologies that strike a nice balance between statistical and computational properties.