基于残差加权经验过程方法的自回归模型分布变点监测

Monitoring Distributional Changes in Autoregressive Models Based on Weighted Empirical Process of Residuals

中文关键词:  分布变点  自回归模型  残量的加权经验过程

英文关键词:distributional changes  autoregressive models  weighted empirical process of residuals

中文摘要:

      时间序列模型的分布变点监测是时间序列分析中的一个重要课题.本文基于残差的加权经验过程方法研究了自回归模型误差项的分布变点监测,证明了监测统计量在原假设下的渐近性质.模拟实验研究了监测统计量的有限样本性质,研究结果表明本文方法不仅能监测分布变点还能监测系数和均值变点.最后将该方法应用于一组金融时间序列.

英文摘要:

      Change monitoring of distribution in time series models is an important issue. This paper proposes a procedure for monitoring changes in the error distribution of autoregressive time series, which is based on a weighed empirical process of residuals with weights equal to the regressors. The asymptotic properties of our monitoring statistic are derived under the null hypothesis of no change in distribution. The finite sample properties are investigated by a simulation. As it turns out, the procedure is not only able to detect distributional changes but also changes in the regression coefficient and mean. Finally, we apply the statistic to a groups of financial data.