Minimum Mean Squared Error Estimation of the Noise in Unobserved Component Models
分析不可观测成分模型中噪声的最小均方误差估计与白噪声的差异，发现噪声方差总被低估，且方差越小低估越严重，小方差噪声估计值自相关大，但样本自相关函数仍可作为有效的诊断工具。
In model-based estimation of unobserved components, the minimum mean squared error estimator of the noise component is different from white noise. In this article, some of the differences are analyzed. It is seen how the variance of the component is always underestimated, and the smaller the noise variance, the larger the underestimation. Estimators of small-variance noise components will also have large autocorrelations. Finally, in the context of an application, the sample autocorrelation function of the estimated noise is seen to perform well as a diagnostic tool, even when the variance is small and the series is of relatively short length.