GAUSSIAN ESTIMATION OF MIXED-ORDER CONTINUOUS-TIME DYNAMIC-MODELS WITH UNOBSERVABLE STOCHASTIC TRENDS FROM MIXED STOCK AND FLOW DATA

This paper develops an algorithm for the exact Gaussian estimation of a mixed-order continuous-time dynamic model, with unobservable stochas tic trends, from a sample of mixed stock and flow data. Its applicatio n yields exact maximum likelihood estimates when the innovations are B rownian motion and either the model is closed or the exogenous variabl es are polynomials in time of degree not exceeding two, and it can be expected to yield very good estimates under much more general circumst ances. 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 var iables are a mixture of stocks and flows.