Gaussian Estimation of a Continuous Time Dynamic Model with Common Stochastic Trends
推导了一阶线性随机微分方程系统的精确离散模型和高斯似然函数，该系统由可观测的随机趋势向量和平稳创新向量驱动。
We derive the exact discrete model and the Gaussian likelihood function of a first-order system of linear stochastic differential equations driven by an observable vector of stochastic trends and a vector of stationary innovations.