This paper describes a moments estimator for a standard state-space model with coefficients generated by a random walk. The method calculates the conditional expectations of the coefficients, given the observations. A penalized least squares estimation is linked to the GLS (Aitken) estimates of the corresponding linear model with time-invariant parameters. The estimates are moments estimates. They do not require the disturbances to be Gaussian, but if they are, the estimates are asymptotically equivalent to maximum likelihood estimates. In contrast to Kalman filtering, no specification of an initial state or an initial covariance matrix is required. While the Kalman filter is one sided, the filter proposed here is two sided and therefore uses more of the available information for estimating intermediate states. Further, the proposed filter has a clear descriptive interpretation.

本文描述了一种标准状态空间模型的矩估计量，该模型具有由随机游走生成的系数。给定观察值，该方法计算系数的条件期望值。惩罚最小二乘估计与具有时不变参数的相应线性模型的GLS（Aitken）估计链接。估计是瞬间估计。它们不需要扰动是高斯的，但是如果是，则估计在渐近上等效于最大似然估计。与卡尔曼滤波相反，不需要指定初始状态或初始协方差矩阵。卡尔曼滤波器是单面的，此处提出的滤波器是双面的，因此使用更多可用信息来估计中间状态。进一步，