Efficient Estimation of the Parameter Path in Unstable Time Series Models
研究了参数随时间适度变化的非线性非高斯模型的推断问题，提出可用人工线性高斯模型近似样本信息，从而得到计算便捷的路径估计和稳定性检验，并能在模型设定错误时保持决策质量。
The paper investigates inference in non-linear and non-Gaussian models with moderately time-varying parameters. We show that for many decision problems, the sample information about the parameter path can be summarized by an artificial linear and Gaussian model, at least asymptotically. The approximation allows for computationally convenient path estimators and parameter stability tests. Also, in contrast to standard Bayesian techniques, the artificial model can be robustified so that in misspecified models, decisions about the path of the (pseudo-true) parameter remain as good as in a corresponding correctly specified model.