近日，以我院统计学系王国长副教授为第一作者的论文在Journal of Business & Economic Statistics在线发表，论文题目为"Testing for the martingale difference hypothesis in multivariate time series models"，合作者为香港大学朱柯助理教授和美国伊利诺伊大学厄巴纳-香槟分校邵晓峰教授。该期刊为统计学与计量经济学领域的国际权威期刊。
论 文 摘 要
This paper proposes a general class of tests to examine whether the error term is a martingale difference sequence in a multivariate time series model with parametric conditional mean. These new tests are formed based on recently developed martingale difference divergence matrix (MDDM), and they provide formal tools to test the multivariate martingale hypothesis in the literature for the first time. Under suitable conditions, the asymptotic null distributions of these MDDM-based tests are established. Moreover, these MDDM-based tests are consistent to detect a broad class of fixed alternatives, and have nontrivial power against local alternatives of order $n^{-1/2}$, where $n$ is the sample size. Since the asymptotic null distributions depend on the data generating process and the parameter estimation, a wild bootstrap procedure is further proposed to approximate the critical values of these MDDM-based tests, and its theoretical validity is justified. Finally, the usefulness of these MDDM-based tests is illustrated by simulation studies and one real data example.
作 者 简 介
王国长，现任暨南大学经济学院统计学系副教授、硕士生导师。2012年毕业于东北师范大学数学与统计学院统计系，并取得统计学博士学位，2012-2014年在中国科学院应用所从事博士后研究工作，2017-2018年赴香港大学统计与精算系进行了学术访问。主要研究方向为函数型数据分析、时间序列、充分性降维等，迄今为止在Journal of Econometrics, Journal of the Business & Economic Statistics, Statistica Sinica和Scandinavian Journal of Statistics等重要学术期刊发表论文20余篇，主持国家级项目2项，博士后面上项目1项，广东省自然科学基金面上项目1项。
