numerical techniques for maximum likelihood estimation of.pdf

Numerical Techniques for Maximum Likelihood Estimation of Continuous-Time DiffusionProcessesAuthor(s): Garland B. Durham and A. Ronald GallantSource: Journal of Business & Economic Statistics, Vol. 20, No. 3 (Jul., 2002), pp. 297-316Published by: American Statistical AssociationStable URL: ble/essed: 03/12/2010 11:54Your use of the JSTOR archive indicates your acceptance of JSTOR's Terms and Conditions of Use, available ate/info/about/policies/. JSTOR's Terms and Conditions of Use provides, in part, that unlessyou have obtained prior permission, you may not download an entire issue of a journal or multiple copies of articles, and youmay use content in the JSTOR archive only for your personal, mercial contact the publisher regarding any further use of this work. Publisher contact information may be obtained ation/showPublisher?publisherCode= copy of any part of a JSTOR transmission must contain the same copyright notice that appears on the screen or printedpage of such is a not-for-profit service that helps scholars, researchers, and students discover, use, and build upon a wide range ofcontent in a trusted digital archive. We use information technology and tools to increase productivity and facilitate new formsof scholarship. For more information about JSTOR, please contact ******@.American Statistical Association is collaborating with JSTOR to digitize, preserve and extend access to Journalof Business & Economic 's Note: This article is the JBES Invited paper presented at the 2001 Joint Statistical Meetings. Numerical Techniques for Maximum Likelihood Estimation Continuous-Time Diffusion Processes Garland B. DURHAM Department of Economics, University of Iowa, Iowa City, IA 52242-1000 (garland-******@) A. Ronald GALLANT Department of Economics, University of North Carolina, Chapel Hill, NC 27599-3305 (ron_******@) Stochastic differential equations often provide a convenient way to descr