The Estimation of Higher-Order Continuous Time Autoregressive Models
提出一种计算连续时间高阶随机微分方程结构参数的最大似然估计方法，适用于等距或不等距观测、缺失数据及测量误差情形，基于状态空间表示和卡尔曼-布西滤波器。
A method is presented for computing maximum likelihood, or Gaussian, estimators of the structural parameters in a continuous time system of higherorder stochastic differential equations. It is argued that it is computationally efficient in the standard case of exact observations made at equally spaced intervals. Furthermore it can be applied in situations where the observations are at unequally spaced intervals, some observations are missing and/or the endogenous variables are subject to measurement error. The method is based on a state space representation and the use of the Kalman–Bucy filter. It is shown how the Kalman-Bucy filter can be modified to deal with flows as well as stocks.