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dc.contributor.authorMcDonald, A. David
dc.contributor.authorSandal, Leif Kristoffer
dc.date.accessioned2006-07-18T09:22:03Z
dc.date.available2006-07-18T09:22:03Z
dc.date.issued1998
dc.identifier.issn1500-4066
dc.identifier.urihttp://hdl.handle.net/11250/163821
dc.description.abstractEstimation of parameters in the drift and diffusion terms of stochastic differential equations involves simulation and generally requires substantial data sets. We examine a method that can be applied when available time series are limited to less than 20 observations per replication. We compare and contrast parameter estimation for linear and nonlinear first-order stochastic differential equations using two criterion functions: one based on a Chi-square statistic, put forward by Hurn and Lindsay (1997), and one based on the Kolmogorov-Smirnov statistic. The estimates generated appear to be precise for all models examined, especially when using the Kolmogorov-Smirnov criterion function.en
dc.format.extent190188 bytes
dc.format.mimetypeapplication/pdf
dc.language.isoengen
dc.publisherNorwegian School of Economics and Business Administration. Department of Finance and Management Scienceen
dc.relation.ispartofseriesDiscussion paperen
dc.relation.ispartofseries1998:10en
dc.titleEstimating the parameters of stochastic differential equations using a criterion function based on the Kolmogorov-Smirnov statisticen
dc.typeWorking paperen


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