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Asymptotic Normmality of Maximum Likelihood Estimators Obtained from Normally Distributed but Dependent Observations

In this article we aim to establish intuitively appealing and verifiable conditions for the first-order efficiency and asymptotic normality of ML estimators in a multi-parameter framework, assuming joint normality but neither the independence nor the identical distribution of the observations. We present five theorems (and a large number of lemmas and propositions), each being a special case of its predecessor.
Year of publication:
1986
Authors: Heijmans, Risto D. H. ; Magnus, Jan R.
Published in:
Econometric Theory. - Cambridge University Press. - Vol. 2.1986, 03, p. 374-412
Publisher: Cambridge University Press
Description of contents: Abstract [journals.cambridge.org]
Type of publication: Article
Source:
Persistent link: https://www.econbiz.de/10008739806
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