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Maximum Likelihood Estimation of Generalized Ito Processes with Discretely Sampled DataAndrew W. LoMassachusetts Institute of Technology (MIT) - Sloan School of Management; Massachusetts Institute of Technology (MIT) - Computer Science and Artificial Intelligence Laboratory (CSAIL); National Bureau of Economic Research (NBER) August 1986 NBER Working Paper No. t0059 Abstract: 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.
Number of Pages in PDF File: 32 working papers seriesDate posted: June 27, 2007Suggested CitationContact Information
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