Gaussian Estimation of Mixed-Order Continuous-Time Dynamic Models with Unobservable Stochastic Trends from Mixed Stock and Flow Data
Gaussian Estimation of Mixed-Order Continuous-Time Dynamic Models with Unobservable Stochastic Trends from Mixed Stock and Flow Data
复制标题
混合存量和流量数据具有不可观测随机趋势的混合阶连续时间动态模型的高斯估计
作者：
A. Bergstrom
本文提出了一种基于混合料流数据样本的具有不可观测随机趋势的混合阶连续时间动态模型的精确高斯估计算法。当创新是布朗运动，或者模型是封闭的，或者外生变量在不超过2度的时间内是多项式时，它的应用产生精确的最大似然估计，并且可以预期在更一般的情况下产生非常好的估计。当模型包含一阶和二阶微分方程的混合，并且内生变量和外生变量都是存量和流量的混合时，本文包括算法实现的详细公式。
This paper develops an algorithm for the exact Gaussian estimation of a mixed-order continuous-time dynamic model, with unobservable stochastic trends, from a sample of mixed stock and flow data. Its application yields exact maximum likelihood estimates when the innovations are Brownian motion and either the model is closed or the exogenous variables are polynomials in time of degree not exceeding two, and it can be expected to yield very good estimates under much more general circumstances. The paper includes detailed formulae for the implementation of the algorithm, when the model comprises a mixture of first- and second-order differential equations and both the endogenous and exogenous variables are a mixture of stocks and flows.