Estimating Missing Observations in Economic Time Series
研究了ARIMA模型中参数的最大似然估计和缺失观测值的估计问题，通过状态空间模型和卡尔曼滤波解决，适用于经济数据不完整的情况。
Abstract Two related problems are considered. The first concerns the maximum likelihood estimation of the parameters in an ARIMA model when some of the observations are missing or subject to temporal aggregation. The second concerns the estimation of the missing observations. Both problems can be solved by setting up the model in state space form and applying the Kalman filter.