A quasi-maximum likelihood method for estimating the parameters of multivariate diffusions
AbstractA quasi-maximum likelihood procedure for estimating the parameters of multi-dimensional diffusions is developed in which the transitional density is a multivariate Gaussian density with first and second moments approximating the true moments of the unknown density. For affine drift and diffusion functions, the moments are exactly those of the true transitional density and for nonlinear drift and diffusion functions the approximation is extremely good and is as effective as alternative methods based on likelihood approximations. The estimation procedure generalises to models with latent factors. A conditioning procedure is developed that allows parameter estimation in the absence of proxies.
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Bibliographic InfoArticle provided by Elsevier in its journal Journal of Econometrics.
Volume (Year): 172 (2013)
Issue (Month): 1 ()
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Web page: http://www.elsevier.com/locate/jeconom
Stochastic differential equations; Parameter estimation; Quasi-maximum likelihood; Moments;
Other versions of this item:
- Stan Hurn & Andrew McClelland & Kenneth Lindsay, 2010. "A quasi-maximum likelihood method for estimating the parameters of multivariate diffusions," NCER Working Paper Series 65, National Centre for Econometric Research.
- C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models
- C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection
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