报告题目： Model averaging prediction for time series models with a diverging number of parameters

报 告 人：Guohua Zou 教授 首都师范大学

报告时间：2019年7月11日上午10:40-11:20

报告地点：数学楼一楼第一报告厅

报告摘要:

       An important problem with model averaging approach is the choice of weights. In this paper, a generalized Mallows model averaging (GMMA) criterion for choosing weights is developed in the context of an infinite order autoregressive (AR(infinity)) process. The GMMA method adapts to the circumstances in which the dimensions of candidate models can be large and increase with the sample size. The GMMA method is shown to be asymptotically optimal in the sense of obtaining the best out-of-sample mean-squared prediction error (MSPE) for both the independent-realization and the same-realization predictions, which, as a byproduct, solves a conjecture put forward by Hansen (2008) that the well-known Mallows model averaging (MMA) criterion from Hansen (2007) is asymptotically optimal for predicting the future of a times series. The rate of the GMMA based weight estimator tending to the optimal weight vector minimizing the independent-realization MSPE is derived as well. Both simulation experiment and real data analysis illustrate the merits of GMMA method in the prediction of AR(infinity) process.

报告人简介：

       Zou教授于1995年获得中国科学院系统科学研究所统计学博士学位。他获得国家杰出青年科学基金项目资助。 他的主要研究兴趣包括利用统计理论和方法来分析实际的经济，医学和遗传数据。他的研究领域包括统计模型选择和平均，调查抽样，统计决策理论和统计遗传学。 他特别关注的是混合效应模型，预测试估计和计量经济学预测因子和测试的敏感性，估计量和预测因子的最优性，如可接受性和极小性，调查中的设计和数据分析，以及疾病和基因之间的联系和关联研究。