Asymptotically optimal estimation in misspecified time series models
研究了当误设的参数时间序列模型拟合到平稳过程时，如何定义渐近有效估计，并证明了若干最小距离估计的有效性，同时考察了高斯极大似然估计的行为。
A concept of asymptotically efficient estimation is presented when a misspecified parametric time series model is fitted to a stationary process. Efficiency of several minimum distance estimates is proved and the behavior of the Gaussian maximum likelihood estimate is studied. Furthermore, the behavior of estimates that minimize the h-step prediction error is discussed briefly. The paper answers to some extent the question what happens when a misspecified model is fitted to time series data and one acts as if the model were true.