缺失数据下ARMA(1, 1)模型的估计方法

近几十年以来,国际上在对"风险的处理和效益的优化"这两个现代金融学的中心议题的分析和处理过程中,金融时间序列的计量学模型及其相应的分析越来越起到非常重要的作用.对于线性时间序列模型如AR(p),MA(q),ARMA(p,q)等,已经为我们所熟知.具体到模型的参数估计在数据没有缺失时,也有很多经典的办法,如最小二乘法、极大似然法等.但是当数据在中间有缺失时,上述方法将无能为力.本文将详细讨论在数据有缺失时的ARMA(1,1)模型,即Zt=αZ_t-1+ε_t-βε_t-1的参数的估计方法.

Estimation Method of ARMA(1,1) Model with Missing Data

In recent decades,when analysis and process two modern financial issues of risk management and effectiveness of the optimization in the international community,the financial time series model and the measurement of the growing corresponding play a very important role.The linear time-series models Have been known such as the AR(p),MA(q),ARMA(p,q).There are a lot of classical methods such as the least-squares method and the maximum likelihood method can estimate parameters of many models except missing data i...