90 citations · 100 across the 17 of their papers we have counts for
7 papers · 1 filter
Matrix GARCH Model: Inference and Application
Cheng Yu, Dong Li, Feiyu Jiang +1
Matrix-variate time series data are largely available in applications. However, no attempt has been made to study their conditional heteroskedasticity that is often observed in eco…
Quantiled conditional variance, skewness, and kurtosis by Cornish-Fisher expansion
Ningning Zhang, Ke Zhu
The conditional variance, skewness, and kurtosis play a central role in time series analysis. These three conditional moments (CMs) are often studied by some parametric models but…
Pair-switching rerandomization
Ke Zhu, Hanzhong Liu
Rerandomization discards assignments with covariates unbalanced in the treatment and control groups to improve estimation and inference efficiency. However, the acceptance-rejectio…
Confidence intervals for parameters in high-dimensional sparse vector autoregression
Ke Zhu, Hanzhong Liu
Vector autoregression (VAR) models are widely used to analyze the interrelationship between multiple variables over time. Estimation and inference for the transition matrices of VA…
Adaptive inference for a semiparametric generalized autoregressive conditional heteroskedasticity model
Feiyu Jiang, Dong Li, Ke Zhu
This paper considers a semiparametric generalized autoregressive conditional heteroskedasticity (S-GARCH) model. For this model, we first estimate the time-varying long run compone…
New HSIC-based tests for independence between two stationary multivariate time series
Guochang Wang, Wai Keung Li, Ke Zhu
This paper proposes some novel one-sided omnibus tests for independence between two multivariate stationary time series. These new tests apply the Hilbert-Schmidt independence crit…
Statistical inference for autoregressive models under heteroscedasticity of unknown form
Ke Zhu
This paper provides an entire inference procedure for the autoregressive model under (conditional) heteroscedasticity of unknown form with a finite variance. We first establish the…