报告时间：2018年12月19日14:30—15:30
报告地点：明德主楼1016会议室
报告题目：Estimation and Inference of Heteroskedasticity Models
with Latent Semiparametric Factors for Multivariate Time Series
报告摘要：
This paper considers estimation and inference of a flexible heteroskedasticity model for multivariate time series, which employs semiparametric latent factors to simultaneously account for the heteroskedasticity and contemporaneous correlations. Specifically, the heteroskedasticity is modeled by the product of unobserved stationary processes of factors and subject-specific covariate effects. Serving as the loadings, the covariate effects are further modeled through additive models. We propose a two-step procedure for estimation. First, the latent processes of factors and their nonparametric loadings are estimated via projection-based methods. The estimation of regression coefficients is further conducted through generalized least squares. Theoretical validity of the two-step procedure is documented. By carefully examining the convergence rates for estimating the latent processes of factors and their loadings, we further study the asymptotic properties of the estimated regression coefficients. In particular, we establish the asymptotic normality of the proposed two-step estimates of regression coefficients. The proposed regression coefficient estimator is also shown to be asymptotically efficient. This leads us to a more efficient confidence set of the regression coefficients. Using a comprehensive simulation study, we demonstrate the finite sample performance of the proposed procedure, and numerical results corroborate our theoretical findings. Finally, we illustrate the use of our proposal through applications to a variety of real data-sets.
个人简介：
Lyuou Zhang is a fifth-year PhD student in Colorado State University. He has been working with Wen Zhou and Haonan Wang. And his research interest is on nonparametric and semiparametric models. He has finished some works on semiparametric latent factor model, latent semiparametric mixture models and detecting spurious discoveries for sparse signals.