The Estimation of Parameters in Nonstationary Higher Order Continuous-Time Dynamic Models
提出一种高效算法，用于计算非平稳高阶连续时间动态模型中结构参数的高斯似然，并应用于参数估计，避免计算观测协方差矩阵，适用于任意阶系统及混合存量流量数据。
This paper is concerned with derivation of a new efficient algorithm for computing the exact Gaussian likelihood for structural parameters in nonstationary higher-order continuous-time dynamic models and with its application in the estimation of these parameters. The algorithm completely avoids the computation of the covariance matrix of the observations and is applicable to a system of any order with mixed stock and flow data. It is used as the basis for an iterative procedure in which the structural parameters and the initial state vector are estimated alternately.