Maximum Likelihood Estimation of Generalized Ito Processes with Discretely Sampled Data
NBER Technical Working Paper No. 59
In this paper, we consider the parametric estimation problem for continuous time stochastic processes described by general first-order nonlinear stochastic differential equations of the Ito type. We characterize the likelihood function of a discretely-sampled set of observations as the solution to a functional partial differential equation. The consistency and asymptotic normality of the maximum likelihood estimators are explored, and several illustrative examples are provided.
Published: Econometric Theory, vol. 4, 1988, pp. 231-247