Maximum Likelihood Estimation of Generalized Ito Processes with Discretely Sampled Data
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.
|Date of creation:||Aug 1986|
|Date of revision:|
|Publication status:||published as Econometric Theory, vol. 4, 1988, pp. 231-247|
|Contact details of provider:|| Postal: National Bureau of Economic Research, 1050 Massachusetts Avenue Cambridge, MA 02138, U.S.A.|
Web page: http://www.nber.org
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