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Küchler, Uwe
Sørensen, Michael M.
Year of Publication: 
Series/Report no.: 
SFB 373 Discussion Paper 1998,45
Multivariate versions of the law of large numbers and the central limit theorem for martingales are given in a generality that is often necessary when studying statistical inference for stochastic process models. To illustrate the usefulness of the results, we consider estimation for a multi-dimensional Gaussian diffusion, where results on consistency and asymptotic normality of the maximum likelihood estimator are obtained in cases that were not covered by previously published limit theorems. The results are also applied to martingales of a different nature, which are typical of the problems occuring in connection with statistical inference for stochastic delay equations.
stochastic delay equations
Central limit theorem
multivariate Gaussian diffusions
likelihood inference
weak law of large numbers
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Working Paper

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