Optimal forecasting of discrete stock and flow data generated by a higher order continuous time system (Q1116609)

This paper is concerned with the development of an algorithm for forecasting discrete stock and flow data generated by a higher order continuous time system from a sample of stock and flow data. The algorithm is shown to be optimal in the sense that the forecasts are exact maximum likelihood estimates of the conditional expectations of the post sample observations, conditional on all the information in the sample, when the innovations are Gaussian. It is also highly efficient computationally when used in conjunction with recently developed estimation methods.