澳门蒲京娱乐场

科学研究
报告题目:

Model averaging prediction for time series models with a diverging number of parameters

报告人:

邹国华 特聘教授(首都师范大学)

报告时间:

报告地点:

数学院二楼报告厅

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.