Gaussian Estimation of Structural Parameters in Higher Order Continuous Time Dynamic Models
研究离散数据下高阶随机微分方程系统结构参数的有效估计，推导精确离散模型，并展示如何用高斯似然最大化计算渐近有效估计。
This paper is concerned with the efficient estimation of structural parameters in closed linear systems of higher order stochastic differential equations when the data are in discrete form and the model generally includes both stock and flow variables. A general existence and uniqueness theorem for the solution of such a system is proved and used in the rigorous derivation of exact discrete models satisfied by the various types of data. It is shown how these models can be used in the computation of various asymptotically efficient estimates obtained by the maximization of the Gaussian likelihood or approximations to it.